diff --git a/Makefile b/Makefile index 55b196c08..5d46136d0 100644 --- a/Makefile +++ b/Makefile @@ -130,7 +130,7 @@ analysis/enrichment/%-results-$(N).yaml: tests/input/genesets/%.yaml $(RUN) ontogpt -vv eval-enrichment -n $(N) -U $< -o $@.tmp && mv $@.tmp $@ analysis/enrichment-summary.yaml: - cat analysis/enrichment/*-$(N)yaml > $@ + cat analysis/enrichment/*-$(N).yaml > $@ analysis/enrichment-summary-$(N).yaml: cat analysis/enrichment/*-$(N).yaml > $@ diff --git a/notebooks/Enrichment-Results-Analysis.ipynb b/notebooks/Enrichment-Results-Analysis.ipynb index 24a7c8694..cdb563d56 100644 --- a/notebooks/Enrichment-Results-Analysis.ipynb +++ b/notebooks/Enrichment-Results-Analysis.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 1, "id": "3452b4b0", "metadata": { "tags": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 2, "id": "97e39d4a", "metadata": {}, "outputs": [], @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 3, "id": "293db399", "metadata": {}, "outputs": [], @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 4, "id": "9b0990c0", "metadata": {}, "outputs": [], @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 5, "id": "e16189b3", "metadata": {}, "outputs": [], @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 6, "id": "ca76b18a", "metadata": {}, "outputs": [], @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 7, "id": "ebab4da5", "metadata": {}, "outputs": [], @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 8, "id": "1eec3e01", "metadata": {}, "outputs": [ @@ -157,7 +157,7 @@ "['GO:0005773', 'GO:0005634', 'GO:0008150']" ] }, - "execution_count": 151, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 9, "id": "1ad7746a", "metadata": {}, "outputs": [ @@ -220,11 +220,10 @@ " source geneset\n", " gene_randomization_factor\n", " has top hit\n", - " rank\n", " in top 5\n", " in top 10\n", + " size overlap\n", " ...\n", - " num terms\n", " num unparsed\n", " go term p values\n", " max p value\n", @@ -234,6 +233,7 @@ " novel\n", " novel labels\n", " num novel\n", + " rank\n", " \n", " \n", " \n", @@ -245,21 +245,21 @@ " 1.0\n", " EDS\n", " 0\n", - " True\n", - " 5.0\n", " False\n", - " True\n", + " False\n", + " False\n", + " 1\n", " ...\n", - " 10\n", - " 10\n", - " [3.341122685366184e-06, 0.00026467316824618385, 1.0, 1.0, 5.591343976855226e-11, 1.0, 1.0, 1.0, 1.0]\n", - " 1.00\n", - " 5.59e-11\n", - " 6.67e-01\n", - " 0.33\n", - " [GO:0033197, GO:0010710, GO:0032966]\n", - " [response to vitamin E, regulation of collagen catabolic process, negative regulation of collagen biosynthetic process]\n", " 3\n", + " [1.0, 3.341122685366184e-06]\n", + " 1.000000\n", + " 3.341123e-06\n", + " 0.500002\n", + " 0.500000\n", + " []\n", + " []\n", + " 0\n", + " NaN\n", " \n", " \n", " 1\n", @@ -270,20 +270,20 @@ " EDS\n", " 0\n", " True\n", - " 0.0\n", " True\n", " True\n", + " 3\n", " ...\n", - " 4\n", - " 4\n", - " [5.591343976855226e-11, 3.341122685366184e-06, 1.0, 0.013894831001982877]\n", - " 1.00\n", - " 5.59e-11\n", - " 2.53e-01\n", - " 0.75\n", - " []\n", - " []\n", - " 0\n", + " 7\n", + " [5.591343976855226e-11, 3.341122685366184e-06,...\n", + " 1.000000\n", + " 5.591344e-11\n", + " 0.571454\n", + " 0.428571\n", + " [GO:0016570]\n", + " [histone modification]\n", + " 1\n", + " 0.0\n", " \n", " \n", " 2\n", @@ -294,20 +294,20 @@ " EDS\n", " 0\n", " False\n", - " NaN\n", " False\n", " False\n", + " 1\n", " ...\n", - " 3\n", - " 3\n", - " [1.0, 3.341122685366184e-06, 1.0]\n", - " 1.00\n", - " 3.34e-06\n", - " 6.67e-01\n", - " 0.33\n", - " []\n", - " []\n", - " 0\n", + " 6\n", + " [1.0, 1.0, 3.341122685366184e-06, 1.0, 1.0, 1.0]\n", + " 1.000000\n", + " 3.341123e-06\n", + " 0.833334\n", + " 0.166667\n", + " [GO:0006457]\n", + " [protein folding]\n", + " 1\n", + " NaN\n", " \n", " \n", " 3\n", @@ -318,20 +318,20 @@ " EDS\n", " 0\n", " False\n", - " NaN\n", " False\n", " False\n", + " 1\n", " ...\n", - " 10\n", - " 10\n", - " [1.0, 1.0, 0.00211738721495877, 0.00119121077281765, 1.0, 0.0003206219640437401, 1.0, 1.0, 1.0]\n", - " 1.00\n", - " 3.21e-04\n", - " 6.67e-01\n", - " 0.33\n", - " [GO:0006457, GO:0098631]\n", - " [protein folding, cell adhesion mediator activity]\n", - " 2\n", + " 6\n", + " [0.00211738721495877]\n", + " 0.002117\n", + " 2.117387e-03\n", + " 0.002117\n", + " 1.000000\n", + " []\n", + " []\n", + " 0\n", + " NaN\n", " \n", " \n", " 4\n", @@ -341,21 +341,21 @@ " 1.0\n", " EDS\n", " 0\n", - " False\n", - " NaN\n", - " False\n", - " False\n", + " True\n", + " True\n", + " True\n", + " 2\n", " ...\n", - " 15\n", - " 15\n", - " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n", - " 1.00\n", - " 1.00e+00\n", - " 1.00e+00\n", - " 0.00\n", + " 8\n", + " [1.0622984137290553e-07, 5.591343976855226e-11...\n", + " 1.000000\n", + " 5.591344e-11\n", + " 0.333333\n", + " 0.666667\n", " []\n", " []\n", " 0\n", + " 3.0\n", " \n", " \n", " ...\n", @@ -382,7 +382,7 @@ " ...\n", " \n", " \n", - " 567\n", + " 1535\n", " N/A\n", " standard\n", " tf-downreg-colorectal-1\n", @@ -390,23 +390,23 @@ " tf-downreg-colorectal\n", " 1\n", " True\n", - " 0.0\n", " True\n", " True\n", + " 183\n", " ...\n", - " 181\n", - " 181\n", - " [4.988150254572451e-28, 5.089713278078831e-28, 6.222732503665588e-28, 2.5181292279890926e-26, 6.360582827326825e-26, 6.280681710633612e-25, 1.992067137890145e-24, 2.160407927771538e-24, 1.531197330767613e-23, 4.406686617875145e-23, 4.520132491659314e-23, 6.047956648850335e-23, 1.42513668286587e-22, 8.082830272613584e-22, 8.280409536451057e-22, 8.457494137576952e-22, 1.1587498886554318e-21, 2.0257524856083073e-21, 2.0803834350443526e-21, 3.470732713187183e-21, 1.463206966124319e-20, 2.506621557484459e-20, 3.054326526778521e-20, 4.839153573691595e-20, 5.101482454717162e-20, 7.808298808825646e-20, 8.868663592283121e-20, 9.993781841681446e-20, 1.0673363321867488e-19, 1.4998597499692998e-19, 1.4998597499692998e-19, 1.5457242764238197e-19, 1.6672250732819605e-19, 1.7114716194746733e-19, 2.406266603748282e-19, 2.4372228200848596e-19, 6.105038238036257e-19, 6.223983270815405e-19, 2.1235204181331734e-18, 2.4355875541881905e-18, 3.662762983506111e-18, 7.451648490886942e-18, 8.396417265824472e-18, 1.0890888827682343e-17, 1.8698967218267112e-17, 2.2078693923148846e-17, 2.5929122360808936e-17, 2.6263604428984028e-17, 3.3376438896625706e-17, 6.312097805142557e-17, 8.44210610764168e-17, 8.780853612258093e-17, 1.256794485435431e-16, 1.3634794047439367e-16, 1.7837718872524886e-16, 2.4682080854405927e-16, 2.528147790145545e-16, 2.84294367797696e-16, 1.3672391972253508e-15, 2.1122706998882423e-15, 2.262163598395585e-15, 2.3769365136746476e-15, 4.807871263219351e-15, 5.098145395239435e-15, 1.2816384484550604e-14, 1.313426426557983e-14, 1.863045482048372e-14, 1.9962778658494635e-14, 3.292811708352154e-14, 4.077674313669671e-14, 6.01177100081973e-14, 2.4957312733595376e-13, 2.864021009704981e-13, 2.9810617943360497e-13, 5.819244888328512e-13, 8.540048474946289e-13, 1.0377360388259339e-12, 1.1099602352756002e-12, 1.1849888758335027e-12, 1.2843592203666209e-12, 1.3338484106013712e-12, 1.3853402724665883e-12, 3.0389767852861082e-12, 7.446509980199845e-12, 9.24102116663525e-12, 1.1186070559309425e-10, 1.142136997598513e-10, 1.142136997598513e-10, 1.142136997598513e-10, 1.5338271728466858e-10, 5.707617974294617e-10, 2.1038940769888833e-09, 2.3785166500474574e-09, 4.56636746104505e-08, 4.896484580823067e-08, 7.071552977186578e-08, 1.1027883530329518e-07, 1.4803215188043313e-07, 2.0238207019879306e-07, 2.40027403455946e-07, ...]\n", - " 0.04\n", - " 4.99e-28\n", - " 4.15e-03\n", - " 1.00\n", + " 184\n", + " [1.655674605612429e-26, 1.6884282099149935e-26...\n", + " 0.048779\n", + " 1.655675e-26\n", + " 0.003470\n", + " 1.000000\n", " []\n", " []\n", " 0\n", + " 0.0\n", " \n", " \n", - " 568\n", + " 1536\n", " N/A\n", " standard_no_ontology\n", " tf-downreg-colorectal-1\n", @@ -414,23 +414,23 @@ " tf-downreg-colorectal\n", " 1\n", " False\n", - " NaN\n", " False\n", " False\n", + " 35\n", " ...\n", - " 38\n", - " 38\n", - " [9.993781841681446e-20, 5.101482454717162e-20, 1.0890888827682343e-17, 3.0389767852861082e-12, 1.2816384484550604e-14, 1.1849888758335027e-12, 1.4998597499692998e-19, 8.082830272613584e-22, 9.24102116663525e-12, 2.5181292279890926e-26, 2.1038940769888833e-09, 1.5338271728466858e-10, 1.4803215188043313e-07, 2.2078693923148846e-17, 8.44210610764168e-17, 4.6074946792391266e-05, 7.698187260692121e-06, 1.992309673557265e-05, 2.772550857289152e-07, 1.0751034116409277e-06, 5.707617974294617e-10, 2.0257524856083073e-21, 4.406686617875145e-23, 2.9810617943360497e-13, 3.054326526778521e-20, 0.0017556171417364651, 5.819244888328512e-13, 0.013364132777108115, 0.01830361381899467, 1.0377360388259339e-12, 1.0, 1.0, 0.04072498754549389, 7.090779938263196e-05, 0.0002413697629437105, 1.0, 1.0, 1.0]\n", - " 1.00\n", - " 2.52e-26\n", - " 1.34e-01\n", - " 0.87\n", + " 39\n", + " [1.0125278971177255e-19, 2.486075176022468e-17...\n", + " 1.000000\n", + " 7.646669e-25\n", + " 0.104709\n", + " 0.897436\n", " []\n", " []\n", " 0\n", + " NaN\n", " \n", " \n", - " 569\n", + " 1537\n", " N/A\n", " random\n", " tf-downreg-colorectal-1\n", @@ -438,23 +438,23 @@ " tf-downreg-colorectal\n", " 1\n", " False\n", - " NaN\n", " False\n", " False\n", + " 8\n", " ...\n", " 49\n", - " 49\n", - " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.101482454717162e-20, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0751034116409277e-06, 1.0, 4.406686617875145e-23, 1.0, 1.0, 1.0, 1.0, 2.5181292279890926e-26, 1.8698967218267112e-17, 1.5338271728466858e-10, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.24102116663525e-12, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.1849888758335027e-12, 1.0, 1.0, 1.0, 1.0]\n", - " 1.00\n", - " 2.52e-26\n", - " 8.37e-01\n", - " 0.16\n", - " [GO:0021549, GO:0050911, GO:0035608, GO:1904261, GO:0005789, GO:0030218, GO:0007080, GO:0043560, GO:1905370, GO:0000725, GO:0000028, GO:2000270, GO:0000049, GO:0060687, GO:0015631, GO:1902774]\n", - " [cerebellum development, detection of chemical stimulus involved in sensory perception of smell, protein deglutamylation, positive regulation of basement membrane assembly involved in embryonic body morphogenesis, endoplasmic reticulum membrane, erythrocyte differentiation, mitotic metaphase plate congression, insulin receptor substrate binding, serine-type endopeptidase complex, recombinational repair, ribosomal small subunit assembly, negative regulation of fibroblast apoptotic process, tRNA binding, regulation of branching involved in prostate gland morphogenesis, tubulin binding, late endosome to lysosome transport]\n", - " 16\n", + " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...\n", + " 1.000000\n", + " 1.012528e-19\n", + " 0.836773\n", + " 0.163265\n", + " [GO:0048289, GO:0000137, GO:0030289, GO:000863...\n", + " [isotype switching to IgE isotypes, Golgi cis ...\n", + " 23\n", + " NaN\n", " \n", " \n", - " 570\n", + " 1538\n", " N/A\n", " rank_based\n", " tf-downreg-colorectal-1\n", @@ -462,23 +462,23 @@ " tf-downreg-colorectal\n", " 1\n", " False\n", - " NaN\n", " False\n", " False\n", + " 17\n", " ...\n", " 51\n", - " 51\n", - " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.101482454717162e-20, 1.0, 1.0, 2.0257524856083073e-21, 8.082830272613584e-22, 1.0, 1.0, 1.0, 5.746621615633877e-07, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.406686617875145e-23, 1.0, 1.0, 2.5181292279890926e-26, 1.0, 1.5338271728466858e-10, 1.0, 1.0, 0.004817572706188025, 1.0, 1.0, 0.0065935552067459295, 1.4998597499692998e-19, 1.0, 9.993781841681446e-20, 1.0, 2.2078693923148846e-17, 1.0, 1.0, 1.0, 0.0002413697629437105, 1.0, 9.24102116663525e-12, 1.2816384484550604e-14, 1.0890888827682343e-17, 1.0, 1.0, 1.1849888758335027e-12, 1.0, 1.0]\n", - " 1.00\n", - " 2.52e-26\n", - " 6.67e-01\n", - " 0.33\n", - " [GO:0005789, GO:0004930, GO:0005509, GO:0000139, GO:0007186]\n", - " [endoplasmic reticulum membrane, G protein-coupled receptor activity, calcium ion binding, Golgi membrane, G protein-coupled receptor signaling pathway]\n", - " 5\n", + " [1.0, 1.0, 7.646668799811157e-25, 0.0002445456...\n", + " 1.000000\n", + " 7.646669e-25\n", + " 0.667541\n", + " 0.333333\n", + " [GO:0005789, GO:0004930, GO:0005509]\n", + " [endoplasmic reticulum membrane, G protein-cou...\n", + " 3\n", + " NaN\n", " \n", " \n", - " 571\n", + " 1539\n", " N/A\n", " closure\n", " tf-downreg-colorectal-1\n", @@ -486,135 +486,122 @@ " tf-downreg-colorectal\n", " 1\n", " True\n", - " 46.0\n", " False\n", " False\n", + " 183\n", " ...\n", - " 3965\n", - " 3965\n", - " [1.0, 1.0, 1.0, 1.0, 1.0, 3.839829331449604e-05, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.1027883530329518e-07, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.988150254572451e-28, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.40027403455946e-07, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...]\n", - " 1.00\n", - " 4.99e-28\n", - " 9.38e-01\n", - " 0.06\n", + " 3976\n", + " [1.0, 1.0, 1.0, 0.004066057825346609, 1.0, 0.0...\n", + " 1.000000\n", + " 1.655675e-26\n", + " 0.937488\n", + " 0.062723\n", " []\n", " []\n", " 0\n", + " 20.0\n", " \n", " \n", "\n", - "

572 rows × 30 columns

\n", + "

1540 rows × 30 columns

\n", "" ], "text/plain": [ - " model method geneset \\\n", - "0 gpt-3.5-turbo no_synopsis EDS-0 \n", - "1 gpt-3.5-turbo ontological_synopsis EDS-0 \n", - "2 gpt-3.5-turbo narrative_synopsis EDS-0 \n", - "3 text-davinci-003 no_synopsis EDS-0 \n", - "4 text-davinci-003 ontological_synopsis EDS-0 \n", - ".. ... ... ... \n", - "567 N/A standard tf-downreg-colorectal-1 \n", - "568 N/A standard_no_ontology tf-downreg-colorectal-1 \n", - "569 N/A random tf-downreg-colorectal-1 \n", - "570 N/A rank_based tf-downreg-colorectal-1 \n", - "571 N/A closure tf-downreg-colorectal-1 \n", - "\n", - " truncation factor source geneset gene_randomization_factor \\\n", - "0 1.0 EDS 0 \n", - "1 1.0 EDS 0 \n", - "2 1.0 EDS 0 \n", - "3 1.0 EDS 0 \n", - "4 1.0 EDS 0 \n", - ".. ... ... ... \n", - "567 1.0 tf-downreg-colorectal 1 \n", - "568 1.0 tf-downreg-colorectal 1 \n", - "569 1.0 tf-downreg-colorectal 1 \n", - "570 1.0 tf-downreg-colorectal 1 \n", - "571 1.0 tf-downreg-colorectal 1 \n", + " model method geneset \\\n", + "0 gpt-3.5-turbo no_synopsis EDS-0 \n", + "1 gpt-3.5-turbo ontological_synopsis EDS-0 \n", + "2 gpt-3.5-turbo narrative_synopsis EDS-0 \n", + "3 text-davinci-003 no_synopsis EDS-0 \n", + "4 text-davinci-003 ontological_synopsis EDS-0 \n", + "... ... ... ... \n", + "1535 N/A standard tf-downreg-colorectal-1 \n", + "1536 N/A standard_no_ontology tf-downreg-colorectal-1 \n", + "1537 N/A random tf-downreg-colorectal-1 \n", + "1538 N/A rank_based tf-downreg-colorectal-1 \n", + "1539 N/A closure tf-downreg-colorectal-1 \n", "\n", - " has top hit rank in top 5 in top 10 ... num terms num unparsed \\\n", - "0 True 5.0 False True ... 10 10 \n", - "1 True 0.0 True True ... 4 4 \n", - "2 False NaN False False ... 3 3 \n", - "3 False NaN False False ... 10 10 \n", - "4 False NaN False False ... 15 15 \n", - ".. ... ... ... ... ... ... ... \n", - "567 True 0.0 True True ... 181 181 \n", - "568 False NaN False False ... 38 38 \n", - "569 False NaN False False ... 49 49 \n", - "570 False NaN False False ... 51 51 \n", - "571 True 46.0 False False ... 3965 3965 \n", + " truncation factor source geneset gene_randomization_factor \\\n", + "0 1.0 EDS 0 \n", + "1 1.0 EDS 0 \n", + "2 1.0 EDS 0 \n", + "3 1.0 EDS 0 \n", + "4 1.0 EDS 0 \n", + "... ... ... ... \n", + "1535 1.0 tf-downreg-colorectal 1 \n", + "1536 1.0 tf-downreg-colorectal 1 \n", + "1537 1.0 tf-downreg-colorectal 1 \n", + "1538 1.0 tf-downreg-colorectal 1 \n", + "1539 1.0 tf-downreg-colorectal 1 \n", "\n", - " go term p values \\\n", - "0 [3.341122685366184e-06, 0.00026467316824618385, 1.0, 1.0, 5.591343976855226e-11, 1.0, 1.0, 1.0, 1.0] \n", - "1 [5.591343976855226e-11, 3.341122685366184e-06, 1.0, 0.013894831001982877] \n", - "2 [1.0, 3.341122685366184e-06, 1.0] \n", - "3 [1.0, 1.0, 0.00211738721495877, 0.00119121077281765, 1.0, 0.0003206219640437401, 1.0, 1.0, 1.0] \n", - "4 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] \n", - ".. ... \n", - "567 [4.988150254572451e-28, 5.089713278078831e-28, 6.222732503665588e-28, 2.5181292279890926e-26, 6.360582827326825e-26, 6.280681710633612e-25, 1.992067137890145e-24, 2.160407927771538e-24, 1.531197330767613e-23, 4.406686617875145e-23, 4.520132491659314e-23, 6.047956648850335e-23, 1.42513668286587e-22, 8.082830272613584e-22, 8.280409536451057e-22, 8.457494137576952e-22, 1.1587498886554318e-21, 2.0257524856083073e-21, 2.0803834350443526e-21, 3.470732713187183e-21, 1.463206966124319e-20, 2.506621557484459e-20, 3.054326526778521e-20, 4.839153573691595e-20, 5.101482454717162e-20, 7.808298808825646e-20, 8.868663592283121e-20, 9.993781841681446e-20, 1.0673363321867488e-19, 1.4998597499692998e-19, 1.4998597499692998e-19, 1.5457242764238197e-19, 1.6672250732819605e-19, 1.7114716194746733e-19, 2.406266603748282e-19, 2.4372228200848596e-19, 6.105038238036257e-19, 6.223983270815405e-19, 2.1235204181331734e-18, 2.4355875541881905e-18, 3.662762983506111e-18, 7.451648490886942e-18, 8.396417265824472e-18, 1.0890888827682343e-17, 1.8698967218267112e-17, 2.2078693923148846e-17, 2.5929122360808936e-17, 2.6263604428984028e-17, 3.3376438896625706e-17, 6.312097805142557e-17, 8.44210610764168e-17, 8.780853612258093e-17, 1.256794485435431e-16, 1.3634794047439367e-16, 1.7837718872524886e-16, 2.4682080854405927e-16, 2.528147790145545e-16, 2.84294367797696e-16, 1.3672391972253508e-15, 2.1122706998882423e-15, 2.262163598395585e-15, 2.3769365136746476e-15, 4.807871263219351e-15, 5.098145395239435e-15, 1.2816384484550604e-14, 1.313426426557983e-14, 1.863045482048372e-14, 1.9962778658494635e-14, 3.292811708352154e-14, 4.077674313669671e-14, 6.01177100081973e-14, 2.4957312733595376e-13, 2.864021009704981e-13, 2.9810617943360497e-13, 5.819244888328512e-13, 8.540048474946289e-13, 1.0377360388259339e-12, 1.1099602352756002e-12, 1.1849888758335027e-12, 1.2843592203666209e-12, 1.3338484106013712e-12, 1.3853402724665883e-12, 3.0389767852861082e-12, 7.446509980199845e-12, 9.24102116663525e-12, 1.1186070559309425e-10, 1.142136997598513e-10, 1.142136997598513e-10, 1.142136997598513e-10, 1.5338271728466858e-10, 5.707617974294617e-10, 2.1038940769888833e-09, 2.3785166500474574e-09, 4.56636746104505e-08, 4.896484580823067e-08, 7.071552977186578e-08, 1.1027883530329518e-07, 1.4803215188043313e-07, 2.0238207019879306e-07, 2.40027403455946e-07, ...] \n", - "568 [9.993781841681446e-20, 5.101482454717162e-20, 1.0890888827682343e-17, 3.0389767852861082e-12, 1.2816384484550604e-14, 1.1849888758335027e-12, 1.4998597499692998e-19, 8.082830272613584e-22, 9.24102116663525e-12, 2.5181292279890926e-26, 2.1038940769888833e-09, 1.5338271728466858e-10, 1.4803215188043313e-07, 2.2078693923148846e-17, 8.44210610764168e-17, 4.6074946792391266e-05, 7.698187260692121e-06, 1.992309673557265e-05, 2.772550857289152e-07, 1.0751034116409277e-06, 5.707617974294617e-10, 2.0257524856083073e-21, 4.406686617875145e-23, 2.9810617943360497e-13, 3.054326526778521e-20, 0.0017556171417364651, 5.819244888328512e-13, 0.013364132777108115, 0.01830361381899467, 1.0377360388259339e-12, 1.0, 1.0, 0.04072498754549389, 7.090779938263196e-05, 0.0002413697629437105, 1.0, 1.0, 1.0] \n", - "569 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.101482454717162e-20, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0751034116409277e-06, 1.0, 4.406686617875145e-23, 1.0, 1.0, 1.0, 1.0, 2.5181292279890926e-26, 1.8698967218267112e-17, 1.5338271728466858e-10, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 9.24102116663525e-12, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.1849888758335027e-12, 1.0, 1.0, 1.0, 1.0] \n", - "570 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 5.101482454717162e-20, 1.0, 1.0, 2.0257524856083073e-21, 8.082830272613584e-22, 1.0, 1.0, 1.0, 5.746621615633877e-07, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.406686617875145e-23, 1.0, 1.0, 2.5181292279890926e-26, 1.0, 1.5338271728466858e-10, 1.0, 1.0, 0.004817572706188025, 1.0, 1.0, 0.0065935552067459295, 1.4998597499692998e-19, 1.0, 9.993781841681446e-20, 1.0, 2.2078693923148846e-17, 1.0, 1.0, 1.0, 0.0002413697629437105, 1.0, 9.24102116663525e-12, 1.2816384484550604e-14, 1.0890888827682343e-17, 1.0, 1.0, 1.1849888758335027e-12, 1.0, 1.0] \n", - "571 [1.0, 1.0, 1.0, 1.0, 1.0, 3.839829331449604e-05, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.1027883530329518e-07, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.988150254572451e-28, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.40027403455946e-07, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...] \n", + " has top hit in top 5 in top 10 size overlap ... num unparsed \\\n", + "0 False False False 1 ... 3 \n", + "1 True True True 3 ... 7 \n", + "2 False False False 1 ... 6 \n", + "3 False False False 1 ... 6 \n", + "4 True True True 2 ... 8 \n", + "... ... ... ... ... ... ... \n", + "1535 True True True 183 ... 184 \n", + "1536 False False False 35 ... 39 \n", + "1537 False False False 8 ... 49 \n", + "1538 False False False 17 ... 51 \n", + "1539 True False False 183 ... 3976 \n", "\n", - " max p value min p value mean p value proportion significant \\\n", - "0 1.00 5.59e-11 6.67e-01 0.33 \n", - "1 1.00 5.59e-11 2.53e-01 0.75 \n", - "2 1.00 3.34e-06 6.67e-01 0.33 \n", - "3 1.00 3.21e-04 6.67e-01 0.33 \n", - "4 1.00 1.00e+00 1.00e+00 0.00 \n", - ".. ... ... ... ... \n", - "567 0.04 4.99e-28 4.15e-03 1.00 \n", - "568 1.00 2.52e-26 1.34e-01 0.87 \n", - "569 1.00 2.52e-26 8.37e-01 0.16 \n", - "570 1.00 2.52e-26 6.67e-01 0.33 \n", - "571 1.00 4.99e-28 9.38e-01 0.06 \n", + " go term p values max p value \\\n", + "0 [1.0, 3.341122685366184e-06] 1.000000 \n", + "1 [5.591343976855226e-11, 3.341122685366184e-06,... 1.000000 \n", + "2 [1.0, 1.0, 3.341122685366184e-06, 1.0, 1.0, 1.0] 1.000000 \n", + "3 [0.00211738721495877] 0.002117 \n", + "4 [1.0622984137290553e-07, 5.591343976855226e-11... 1.000000 \n", + "... ... ... \n", + "1535 [1.655674605612429e-26, 1.6884282099149935e-26... 0.048779 \n", + "1536 [1.0125278971177255e-19, 2.486075176022468e-17... 1.000000 \n", + "1537 [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ... 1.000000 \n", + "1538 [1.0, 1.0, 7.646668799811157e-25, 0.0002445456... 1.000000 \n", + "1539 [1.0, 1.0, 1.0, 0.004066057825346609, 1.0, 0.0... 1.000000 \n", "\n", - " novel \\\n", - "0 [GO:0033197, GO:0010710, GO:0032966] \n", - "1 [] \n", - "2 [] \n", - "3 [GO:0006457, GO:0098631] \n", - "4 [] \n", - ".. ... \n", - "567 [] \n", - "568 [] \n", - "569 [GO:0021549, GO:0050911, GO:0035608, GO:1904261, GO:0005789, GO:0030218, GO:0007080, GO:0043560, GO:1905370, GO:0000725, GO:0000028, GO:2000270, GO:0000049, GO:0060687, GO:0015631, GO:1902774] \n", - "570 [GO:0005789, GO:0004930, GO:0005509, GO:0000139, GO:0007186] \n", - "571 [] \n", + " min p value mean p value proportion significant \\\n", + "0 3.341123e-06 0.500002 0.500000 \n", + "1 5.591344e-11 0.571454 0.428571 \n", + "2 3.341123e-06 0.833334 0.166667 \n", + "3 2.117387e-03 0.002117 1.000000 \n", + "4 5.591344e-11 0.333333 0.666667 \n", + "... ... ... ... \n", + "1535 1.655675e-26 0.003470 1.000000 \n", + "1536 7.646669e-25 0.104709 0.897436 \n", + "1537 1.012528e-19 0.836773 0.163265 \n", + "1538 7.646669e-25 0.667541 0.333333 \n", + "1539 1.655675e-26 0.937488 0.062723 \n", "\n", - " novel labels \\\n", - "0 [response to vitamin E, regulation of collagen catabolic process, negative regulation of collagen biosynthetic process] \n", - "1 [] \n", - "2 [] \n", - "3 [protein folding, cell adhesion mediator activity] \n", - "4 [] \n", - ".. ... \n", - "567 [] \n", - "568 [] \n", - "569 [cerebellum development, detection of chemical stimulus involved in sensory perception of smell, protein deglutamylation, positive regulation of basement membrane assembly involved in embryonic body morphogenesis, endoplasmic reticulum membrane, erythrocyte differentiation, mitotic metaphase plate congression, insulin receptor substrate binding, serine-type endopeptidase complex, recombinational repair, ribosomal small subunit assembly, negative regulation of fibroblast apoptotic process, tRNA binding, regulation of branching involved in prostate gland morphogenesis, tubulin binding, late endosome to lysosome transport] \n", - "570 [endoplasmic reticulum membrane, G protein-coupled receptor activity, calcium ion binding, Golgi membrane, G protein-coupled receptor signaling pathway] \n", - "571 [] \n", + " novel \\\n", + "0 [] \n", + "1 [GO:0016570] \n", + "2 [GO:0006457] \n", + "3 [] \n", + "4 [] \n", + "... ... \n", + "1535 [] \n", + "1536 [] \n", + "1537 [GO:0048289, GO:0000137, GO:0030289, GO:000863... \n", + "1538 [GO:0005789, GO:0004930, GO:0005509] \n", + "1539 [] \n", "\n", - " num novel \n", - "0 3 \n", - "1 0 \n", - "2 0 \n", - "3 2 \n", - "4 0 \n", - ".. ... \n", - "567 0 \n", - "568 0 \n", - "569 16 \n", - "570 5 \n", - "571 0 \n", + " novel labels num novel rank \n", + "0 [] 0 NaN \n", + "1 [histone modification] 1 0.0 \n", + "2 [protein folding] 1 NaN \n", + "3 [] 0 NaN \n", + "4 [] 0 3.0 \n", + "... ... ... ... \n", + "1535 [] 0 0.0 \n", + "1536 [] 0 NaN \n", + "1537 [isotype switching to IgE isotypes, Golgi cis ... 23 NaN \n", + "1538 [endoplasmic reticulum membrane, G protein-cou... 3 NaN \n", + "1539 [] 0 20.0 \n", "\n", - "[572 rows x 30 columns]" + "[1540 rows x 30 columns]" ] }, - "execution_count": 152, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -731,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 10, "id": "b377198f", "metadata": {}, "outputs": [ @@ -787,9 +774,9 @@ " ontological_synopsis\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", + " 5\n", + " text-davinci-003\n", + " narrative_synopsis\n", " \n", " \n", " 6\n", @@ -818,7 +805,6 @@ " \n", " \n", "\n", - "

11 rows × 2 columns

\n", "" ], "text/plain": [ @@ -828,17 +814,15 @@ "2 gpt-3.5-turbo narrative_synopsis\n", "3 text-davinci-003 no_synopsis\n", "4 text-davinci-003 ontological_synopsis\n", - ".. ... ...\n", + "5 text-davinci-003 narrative_synopsis\n", "6 N/A standard\n", "7 N/A standard_no_ontology\n", "8 N/A random\n", "9 N/A rank_based\n", - "10 N/A closure\n", - "\n", - "[11 rows x 2 columns]" + "10 N/A closure" ] }, - "execution_count": 153, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -849,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 11, "id": "c7263499", "metadata": {}, "outputs": [ @@ -888,63 +872,63 @@ " \n", " \n", " 44\n", - " HALLMARK_APICAL_JUNCTION\n", + " HALLMARK_ADIPOGENESIS\n", " \n", " \n", " 66\n", - " HALLMARK_GLYCOLYSIS\n", + " HALLMARK_ALLOGRAFT_REJECTION\n", " \n", " \n", " 88\n", - " HALLMARK_HEDGEHOG_SIGNALING\n", + " HALLMARK_ANDROGEN_RESPONSE\n", " \n", " \n", " ...\n", " ...\n", " \n", " \n", - " 462\n", + " 1430\n", " peroxisome\n", " \n", " \n", - " 484\n", + " 1452\n", " progeria\n", " \n", " \n", - " 506\n", + " 1474\n", " regulation of presynaptic membrane potential\n", " \n", " \n", - " 528\n", + " 1496\n", " sensory ataxia\n", " \n", " \n", - " 550\n", + " 1518\n", " tf-downreg-colorectal\n", " \n", " \n", "\n", - "

26 rows × 1 columns

\n", + "

70 rows × 1 columns

\n", "" ], "text/plain": [ - " source geneset\n", - "0 EDS\n", - "22 FA\n", - "44 HALLMARK_APICAL_JUNCTION\n", - "66 HALLMARK_GLYCOLYSIS\n", - "88 HALLMARK_HEDGEHOG_SIGNALING\n", - ".. ...\n", - "462 peroxisome\n", - "484 progeria\n", - "506 regulation of presynaptic membrane potential\n", - "528 sensory ataxia\n", - "550 tf-downreg-colorectal\n", + " source geneset\n", + "0 EDS\n", + "22 FA\n", + "44 HALLMARK_ADIPOGENESIS\n", + "66 HALLMARK_ALLOGRAFT_REJECTION\n", + "88 HALLMARK_ANDROGEN_RESPONSE\n", + "... ...\n", + "1430 peroxisome\n", + "1452 progeria\n", + "1474 regulation of presynaptic membrane potential\n", + "1496 sensory ataxia\n", + "1518 tf-downreg-colorectal\n", "\n", - "[26 rows x 1 columns]" + "[70 rows x 1 columns]" ] }, - "execution_count": 154, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -955,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 12, "id": "30174f04", "metadata": {}, "outputs": [ @@ -963,7 +947,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/nc/m4tx21912kv1b8nk3zzx9plr0000gn/T/ipykernel_82942/3852654709.py:1: FutureWarning: this method is deprecated in favour of `Styler.hide(axis=\"index\")`\n", + "/var/folders/nc/m4tx21912kv1b8nk3zzx9plr0000gn/T/ipykernel_82661/3852654709.py:1: FutureWarning: this method is deprecated in favour of `Styler.hide(axis=\"index\")`\n", " df[[SOURCE_GENESET, GENESET_SIZE]].drop_duplicates().style.hide_index()\n" ] }, @@ -972,126 +956,302 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
source genesetgeneset_sizesource genesetgeneset_size
EDS19EDS19
FA19
HALLMARK_ADIPOGENESIS200
HALLMARK_ALLOGRAFT_REJECTION200
HALLMARK_ANDROGEN_RESPONSE101
HALLMARK_ANGIOGENESIS36
HALLMARK_APICAL_JUNCTION200
HALLMARK_APICAL_SURFACE44
HALLMARK_APOPTOSIS161
HALLMARK_BILE_ACID_METABOLISM112
HALLMARK_CHOLESTEROL_HOMEOSTASIS74
HALLMARK_COAGULATION138
HALLMARK_COMPLEMENT200
FA19HALLMARK_DNA_REPAIR150
HALLMARK_APICAL_JUNCTION200HALLMARK_E2F_TARGETS200
HALLMARK_GLYCOLYSIS200HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION200
HALLMARK_HEDGEHOG_SIGNALING36HALLMARK_ESTROGEN_RESPONSE_EARLY200
HALLMARK_INTERFERON_GAMMA_RESPONSE200HALLMARK_ESTROGEN_RESPONSE_LATE200
HALLMARK_MTORC1_SIGNALING200HALLMARK_FATTY_ACID_METABOLISM158
T cell proliferation72HALLMARK_G2M_CHECKPOINT200
Yamanaka-TFs4HALLMARK_GLYCOLYSIS200
amigo-example36HALLMARK_HEDGEHOG_SIGNALING36
bicluster_RNAseqDB_0158HALLMARK_HEME_METABOLISM200
bicluster_RNAseqDB_100252HALLMARK_HYPOXIA200
term-GO:000721228HALLMARK_IL2_STAT5_SIGNALING199
endocytosis16HALLMARK_IL6_JAK_STAT3_SIGNALING87
go-postsynapse-calcium-transmembrane33HALLMARK_INFLAMMATORY_RESPONSE200
go-reg-autophagy-pkra17HALLMARK_INTERFERON_ALPHA_RESPONSE97
hydrolase activity, hydrolyzing O-glycosyl compounds91HALLMARK_INTERFERON_GAMMA_RESPONSE200
ig-receptor-binding-202291HALLMARK_KRAS_SIGNALING_DN200
meiosis I54HALLMARK_KRAS_SIGNALING_UP200
molecular sequestering30HALLMARK_MITOTIC_SPINDLE199
mtorc1200HALLMARK_MTORC1_SIGNALING200
peroxisome8HALLMARK_MYC_TARGETS_V1200
progeria4HALLMARK_MYC_TARGETS_V258
regulation of presynaptic membrane potential30HALLMARK_MYOGENESIS200
sensory ataxia15HALLMARK_NOTCH_SIGNALING32
tf-downreg-colorectal51HALLMARK_P53_PATHWAY200
HALLMARK_PANCREAS_BETA_CELLS40
HALLMARK_PEROXISOME104
HALLMARK_PI3K_AKT_MTOR_SIGNALING105
HALLMARK_PROTEIN_SECRETION96
HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY49
HALLMARK_SPERMATOGENESIS135
HALLMARK_TGF_BETA_SIGNALING54
HALLMARK_TNFA_SIGNALING_VIA_NFKB200
HALLMARK_UNFOLDED_PROTEIN_RESPONSE113
HALLMARK_UV_RESPONSE_DN144
HALLMARK_UV_RESPONSE_UP158
HALLMARK_WNT_BETA_CATENIN_SIGNALING42
T cell proliferation72
Yamanaka-TFs4
amigo-example36
bicluster_RNAseqDB_0158
bicluster_RNAseqDB_100252
glycolysis-gocam10
term-GO:000721228
endocytosis16
go-postsynapse-calcium-transmembrane33
go-reg-autophagy-pkra17
hydrolase activity, hydrolyzing O-glycosyl compounds91
ig-receptor-binding-202291
meiosis I54
molecular sequestering30
mtorc1200
peroxisome8
progeria4
regulation of presynaptic membrane potential30
sensory ataxia15
tf-downreg-colorectal51
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 155, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1102,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 13, "id": "0c34babb", "metadata": {}, "outputs": [ @@ -1110,7 +1270,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/nc/m4tx21912kv1b8nk3zzx9plr0000gn/T/ipykernel_82942/2185383538.py:1: FutureWarning: this method is deprecated in favour of `Styler.hide(axis=\"index\")`\n", + "/var/folders/nc/m4tx21912kv1b8nk3zzx9plr0000gn/T/ipykernel_82661/2185383538.py:1: FutureWarning: this method is deprecated in favour of `Styler.hide(axis=\"index\")`\n", " df[[MODEL, METHOD]].drop_duplicates().style.hide_index()\n" ] }, @@ -1119,66 +1279,66 @@ "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
modelmethodmodelmethod
gpt-3.5-turbono_synopsisgpt-3.5-turbono_synopsis
gpt-3.5-turboontological_synopsisgpt-3.5-turboontological_synopsis
gpt-3.5-turbonarrative_synopsisgpt-3.5-turbonarrative_synopsis
text-davinci-003no_synopsistext-davinci-003no_synopsis
text-davinci-003ontological_synopsistext-davinci-003ontological_synopsis
text-davinci-003narrative_synopsistext-davinci-003narrative_synopsis
N/AstandardN/Astandard
N/Astandard_no_ontologyN/Astandard_no_ontology
N/ArandomN/Arandom
N/Arank_basedN/Arank_based
N/AclosureN/Aclosure
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 156, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1197,7 +1357,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 14, "id": "a15bf2ee", "metadata": {}, "outputs": [], @@ -1219,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 15, "id": "854ea86d", "metadata": {}, "outputs": [ @@ -1227,7 +1387,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "193074\n" + "775719\n" ] }, { @@ -1258,13 +1418,13 @@ " goslim_chembl\n", " goslim_generic\n", " goslim_drosophila\n", - " anc_of_goslim_synapse\n", + " goslim_yeast\n", " goslim_pir\n", " goslim_metagenomics\n", " ...\n", - " goslim_flybase_ribbon\n", " anc_of_goslim_flybase_ribbon\n", " anc_of_goslim_agr\n", + " goslim_mouse\n", " anc_of_goslim_pombe\n", " anc_of_gocheck_do_not_manually_annotate\n", " anc_of_goslim_mouse\n", @@ -1279,11 +1439,11 @@ " 0\n", " gpt-3.5-turbo\n", " no_synopsis\n", - " GO:0030198\n", - " extracellular matrix organization\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", + " GO:0032964\n", + " collagen biosynthetic process\n", + " NaN\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -1303,11 +1463,11 @@ " 1\n", " gpt-3.5-turbo\n", " no_synopsis\n", - " GO:0032963\n", - " collagen metabolic process\n", - " NaN\n", - " NaN\n", - " NaN\n", + " GO:0030198\n", + " extracellular matrix organization\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -1326,9 +1486,9 @@ " \n", " 2\n", " gpt-3.5-turbo\n", - " no_synopsis\n", - " GO:0033197\n", - " response to vitamin E\n", + " ontological_synopsis\n", + " GO:0030199\n", + " collagen fibril organization\n", " NaN\n", " NaN\n", " NaN\n", @@ -1350,12 +1510,12 @@ " \n", " 3\n", " gpt-3.5-turbo\n", - " no_synopsis\n", - " GO:0010710\n", - " regulation of collagen catabolic process\n", - " NaN\n", - " NaN\n", - " NaN\n", + " ontological_synopsis\n", + " GO:0030198\n", + " extracellular matrix organization\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -1374,9 +1534,9 @@ " \n", " 4\n", " gpt-3.5-turbo\n", - " no_synopsis\n", - " GO:0030199\n", - " collagen fibril organization\n", + " ontological_synopsis\n", + " GO:0061448\n", + " connective tissue development\n", " NaN\n", " NaN\n", " NaN\n", @@ -1420,11 +1580,11 @@ " ...\n", " \n", " \n", - " 193069\n", + " 775714\n", " N/A\n", " closure\n", - " GO:0032106\n", - " positive regulation of response to extracellular stimulus\n", + " GO:1903580\n", + " positive regulation of ATP metabolic process\n", " NaN\n", " NaN\n", " NaN\n", @@ -1444,11 +1604,11 @@ " NaN\n", " \n", " \n", - " 193070\n", + " 775715\n", " N/A\n", " closure\n", - " GO:0032109\n", - " positive regulation of response to nutrient levels\n", + " GO:2001169\n", + " regulation of ATP biosynthetic process\n", " NaN\n", " NaN\n", " NaN\n", @@ -1468,11 +1628,11 @@ " NaN\n", " \n", " \n", - " 193071\n", + " 775716\n", " N/A\n", " closure\n", - " GO:0045848\n", - " positive regulation of nitrogen utilization\n", + " GO:2001171\n", + " positive regulation of ATP biosynthetic process\n", " NaN\n", " NaN\n", " NaN\n", @@ -1492,11 +1652,11 @@ " NaN\n", " \n", " \n", - " 193072\n", + " 775717\n", " N/A\n", " closure\n", - " GO:2001248\n", - " regulation of ammonia assimilation cycle\n", + " GO:2001198\n", + " regulation of dendritic cell differentiation\n", " NaN\n", " NaN\n", " NaN\n", @@ -1516,11 +1676,11 @@ " NaN\n", " \n", " \n", - " 193073\n", + " 775718\n", " N/A\n", " closure\n", - " GO:2001250\n", - " positive regulation of ammonia assimilation cycle\n", + " GO:2001200\n", + " positive regulation of dendritic cell differen...\n", " NaN\n", " NaN\n", " NaN\n", @@ -1541,87 +1701,74 @@ " \n", " \n", "\n", - "

193074 rows × 36 columns

\n", + "

775719 rows × 36 columns

\n", "" ], "text/plain": [ - " model method term \\\n", - "0 gpt-3.5-turbo no_synopsis GO:0030198 \n", - "1 gpt-3.5-turbo no_synopsis GO:0032963 \n", - "2 gpt-3.5-turbo no_synopsis GO:0033197 \n", - "3 gpt-3.5-turbo no_synopsis GO:0010710 \n", - "4 gpt-3.5-turbo no_synopsis GO:0030199 \n", - "... ... ... ... \n", - "193069 N/A closure GO:0032106 \n", - "193070 N/A closure GO:0032109 \n", - "193071 N/A closure GO:0045848 \n", - "193072 N/A closure GO:2001248 \n", - "193073 N/A closure GO:2001250 \n", + " model method term \\\n", + "0 gpt-3.5-turbo no_synopsis GO:0032964 \n", + "1 gpt-3.5-turbo no_synopsis GO:0030198 \n", + "2 gpt-3.5-turbo ontological_synopsis GO:0030199 \n", + "3 gpt-3.5-turbo ontological_synopsis GO:0030198 \n", + "4 gpt-3.5-turbo ontological_synopsis GO:0061448 \n", + "... ... ... ... \n", + "775714 N/A closure GO:1903580 \n", + "775715 N/A closure GO:2001169 \n", + "775716 N/A closure GO:2001171 \n", + "775717 N/A closure GO:2001198 \n", + "775718 N/A closure GO:2001200 \n", "\n", - " label \\\n", - "0 extracellular matrix organization \n", - "1 collagen metabolic process \n", - "2 response to vitamin E \n", - "3 regulation of collagen catabolic process \n", - "4 collagen fibril organization \n", - "... ... \n", - "193069 positive regulation of response to extracellular stimulus \n", - "193070 positive regulation of response to nutrient levels \n", - "193071 positive regulation of nitrogen utilization \n", - "193072 regulation of ammonia assimilation cycle \n", - "193073 positive regulation of ammonia assimilation cycle \n", + " label goslim_chembl \\\n", + "0 collagen biosynthetic process NaN \n", + "1 extracellular matrix organization 1.0 \n", + "2 collagen fibril organization NaN \n", + "3 extracellular matrix organization 1.0 \n", + "4 connective tissue development NaN \n", + "... ... ... \n", + "775714 positive regulation of ATP metabolic process NaN \n", + "775715 regulation of ATP biosynthetic process NaN \n", + "775716 positive regulation of ATP biosynthetic process NaN \n", + "775717 regulation of dendritic cell differentiation NaN \n", + "775718 positive regulation of dendritic cell differen... NaN \n", "\n", - " goslim_chembl goslim_generic goslim_drosophila \\\n", - "0 1.0 1.0 1.0 \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "... ... ... ... \n", - "193069 NaN NaN NaN \n", - "193070 NaN NaN NaN \n", - "193071 NaN NaN NaN \n", - "193072 NaN NaN NaN \n", - "193073 NaN NaN NaN \n", + " goslim_generic goslim_drosophila goslim_yeast goslim_pir \\\n", + "0 NaN NaN NaN NaN \n", + "1 1.0 1.0 NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 1.0 1.0 NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "... ... ... ... ... \n", + "775714 NaN NaN NaN NaN \n", + "775715 NaN NaN NaN NaN \n", + "775716 NaN NaN NaN NaN \n", + "775717 NaN NaN NaN NaN \n", + "775718 NaN NaN NaN NaN \n", "\n", - " anc_of_goslim_synapse goslim_pir goslim_metagenomics ... \\\n", - "0 NaN NaN NaN ... \n", - "1 NaN NaN NaN ... \n", - "2 NaN NaN NaN ... \n", - "3 NaN NaN NaN ... \n", - "4 NaN NaN NaN ... \n", - "... ... ... ... ... \n", - "193069 NaN NaN NaN ... \n", - "193070 NaN NaN NaN ... \n", - "193071 NaN NaN NaN ... \n", - "193072 NaN NaN NaN ... \n", - "193073 NaN NaN NaN ... \n", + " goslim_metagenomics ... anc_of_goslim_flybase_ribbon \\\n", + "0 NaN ... NaN \n", + "1 NaN ... NaN \n", + "2 NaN ... NaN \n", + "3 NaN ... NaN \n", + "4 NaN ... NaN \n", + "... ... ... ... \n", + "775714 NaN ... NaN \n", + "775715 NaN ... NaN \n", + "775716 NaN ... NaN \n", + "775717 NaN ... NaN \n", + "775718 NaN ... NaN \n", "\n", - " goslim_flybase_ribbon anc_of_goslim_flybase_ribbon \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "193069 NaN NaN \n", - "193070 NaN NaN \n", - "193071 NaN NaN \n", - "193072 NaN NaN \n", - "193073 NaN NaN \n", - "\n", - " anc_of_goslim_agr anc_of_goslim_pombe \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "193069 NaN NaN \n", - "193070 NaN NaN \n", - "193071 NaN NaN \n", - "193072 NaN NaN \n", - "193073 NaN NaN \n", + " anc_of_goslim_agr goslim_mouse anc_of_goslim_pombe \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "... ... ... ... \n", + "775714 NaN NaN NaN \n", + "775715 NaN NaN NaN \n", + "775716 NaN NaN NaN \n", + "775717 NaN NaN NaN \n", + "775718 NaN NaN NaN \n", "\n", " anc_of_gocheck_do_not_manually_annotate anc_of_goslim_mouse \\\n", "0 NaN NaN \n", @@ -1630,11 +1777,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "193069 NaN NaN \n", - "193070 NaN NaN \n", - "193071 NaN NaN \n", - "193072 NaN NaN \n", - "193073 NaN NaN \n", + "775714 NaN NaN \n", + "775715 NaN NaN \n", + "775716 NaN NaN \n", + "775717 NaN NaN \n", + "775718 NaN NaN \n", "\n", " anc_of_goslim_plant gocheck_do_not_annotate \\\n", "0 NaN NaN \n", @@ -1643,11 +1790,11 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "193069 NaN NaN \n", - "193070 NaN NaN \n", - "193071 NaN NaN \n", - "193072 NaN NaN \n", - "193073 NaN NaN \n", + "775714 NaN NaN \n", + "775715 NaN NaN \n", + "775716 NaN NaN \n", + "775717 NaN NaN \n", + "775718 NaN NaN \n", "\n", " gocheck_do_not_manually_annotate goslim_synapse \n", "0 NaN NaN \n", @@ -1656,16 +1803,16 @@ "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", - "193069 NaN NaN \n", - "193070 NaN NaN \n", - "193071 NaN NaN \n", - "193072 NaN NaN \n", - "193073 NaN NaN \n", + "775714 NaN NaN \n", + "775715 NaN NaN \n", + "775716 NaN NaN \n", + "775717 NaN NaN \n", + "775718 NaN NaN \n", "\n", - "[193074 rows x 36 columns]" + "[775719 rows x 36 columns]" ] }, - "execution_count": 158, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1686,7 +1833,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 16, "id": "9745fa79", "metadata": {}, "outputs": [ @@ -1694,47 +1841,47 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1775,401 +1922,401 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  goslim_chemblgoslim_genericgoslim_drosophilaanc_of_goslim_synapsegoslim_pirgoslim_metagenomicsgoslim_pombegoslim_candidaprokaryote_subsetgoslim_yeastanc_of_goslim_pirgoslim_plantgoslim_agrgoslim_mouseanc_of_gocheck_do_not_annotateanc_of_goslim_chemblanc_of_goslim_genericanc_of_goslim_drosophilaanc_of_goslim_candidaanc_of_goslim_metagenomicsanc_of_goslim_yeastanc_of_prokaryote_subsetgoslim_flybase_ribbonanc_of_goslim_flybase_ribbonanc_of_goslim_agranc_of_goslim_pombeanc_of_gocheck_do_not_manually_annotateanc_of_goslim_mouseanc_of_goslim_plantgocheck_do_not_annotategocheck_do_not_manually_annotategoslim_synapsegoslim_chemblgoslim_genericgoslim_drosophilagoslim_yeastgoslim_pirgoslim_metagenomicsgoslim_pombegoslim_candidaprokaryote_subsetanc_of_goslim_synapseanc_of_goslim_yeastanc_of_goslim_piranc_of_goslim_chemblanc_of_goslim_genericanc_of_goslim_drosophilaanc_of_goslim_candidagoslim_plantanc_of_goslim_metagenomicsanc_of_gocheck_do_not_annotateanc_of_prokaryote_subsetgoslim_flybase_ribbongoslim_agranc_of_goslim_flybase_ribbonanc_of_goslim_agrgoslim_mouseanc_of_goslim_pombeanc_of_gocheck_do_not_manually_annotateanc_of_goslim_mouseanc_of_goslim_plantgocheck_do_not_annotategocheck_do_not_manually_annotategoslim_synapse
model
N/Aclosure0.0560.0310.0390.1140.0580.0210.0100.0220.0200.0330.0910.0280.0190.0160.0530.0670.0580.0680.0390.0440.0640.0410.0160.0170.0270.0480.0410.0280.0340.0100.0100.013
random0.1710.1070.1200.1700.1140.0710.0170.0900.0620.1090.1130.1060.0730.0650.0870.0450.0580.0690.0270.0460.0530.0340.0610.0290.0410.0870.0710.0430.0240.0010.0020.014
rank_based0.3710.2160.2460.3410.2360.1460.0250.2020.1190.2290.2230.2420.1800.1530.1810.1010.1340.1680.0670.0960.1090.0800.1380.0570.0910.1640.1480.0940.0640.0000.0030.019
standard0.0830.0490.0590.2520.0980.0440.0150.0380.0380.0440.1660.0590.0420.0340.1110.1540.1390.1580.0900.0990.1310.1000.0370.0450.0740.1020.1140.0790.0800.0330.0350.020
standard_no_ontology0.0820.0560.0620.1130.0570.0310.0130.0330.0360.0450.0570.0400.0360.0280.0400.0230.0240.0300.0100.0290.0200.0140.0270.0080.0140.0290.0190.0160.0100.0010.0010.040
gpt-3.5-turbonarrative_synopsis0.2500.2090.2140.3060.1840.1170.0920.1480.1580.1480.1680.1330.0710.0560.1730.0770.1220.1380.0510.0660.1120.0970.0460.0360.0660.1070.0770.0710.0200.0100.0260.020
no_synopsis0.1670.1600.2030.2270.1700.0900.0870.1030.1100.1470.1600.0670.0300.0170.1230.0700.1030.0930.0430.0870.0900.0900.0130.0070.0630.0730.0500.0670.0530.0270.0100.017
ontological_synopsis0.2120.1680.2160.2240.1560.1040.0760.1360.1240.1560.1440.1120.0680.0600.1120.0560.0720.0800.0400.0760.0800.0560.0440.0320.0520.0320.0440.0560.0280.0080.0080.016
text-davinci-003narrative_synopsis0.3570.2560.3430.2800.2950.2080.1550.2460.2460.2320.2460.2220.1110.1010.2030.0920.1500.1400.0920.1350.1930.1160.0720.0480.1160.0920.0920.1160.0530.0340.0050.005
no_synopsis0.3460.2410.3210.3210.3080.2070.1690.2490.2190.2530.2780.2320.1270.0930.2530.1180.2070.2190.1010.1310.1810.1520.0720.0300.1050.1650.1140.1100.0680.0300.0300.017
ontological_synopsis0.2290.1190.1670.2460.1420.1020.0510.1100.0990.1300.1900.1130.0680.0510.1330.0850.1220.1130.0760.0790.0990.1160.0540.0340.0880.0540.0710.0930.0510.0060.0110.003N/Aclosure0.0490.0270.0340.0290.0500.0180.0090.0170.0170.0930.0500.0780.0540.0450.0530.0300.0220.0350.0420.0320.0120.0150.0120.0190.0120.0370.0310.0200.0260.0080.0070.012
random0.1650.1000.1090.1060.1080.0660.0140.0840.0560.1610.0520.1060.0460.0580.0690.0280.1020.0400.0860.0340.0600.0750.0250.0370.0660.0770.0660.0380.0270.0010.0010.017
rank_based0.3340.1830.2200.2020.2210.1200.0260.1640.1020.3140.1120.2070.1070.1290.1610.0710.1990.0960.1610.0810.1110.1510.0450.0790.1250.1420.1220.0820.0660.0000.0040.022
standard0.0880.0480.0550.0440.0940.0420.0130.0370.0340.2190.1400.1700.1660.1440.1630.0990.0610.1040.1170.1080.0310.0360.0460.0790.0320.1030.1210.0860.0880.0360.0380.007
standard_no_ontology0.1080.0630.0640.0600.0660.0330.0130.0420.0350.0960.0280.0730.0290.0350.0370.0120.0500.0320.0510.0220.0300.0360.0080.0130.0330.0430.0320.0130.0110.0010.0010.014
gpt-3.5-turbonarrative_synopsis0.2510.2120.2330.1870.2260.1250.1100.1480.1320.2420.1090.1670.0710.1120.1210.0530.1170.0870.1740.0980.0410.0680.0270.0600.0480.0800.0910.0690.0300.0200.0210.004
no_synopsis0.1940.1810.2160.1470.2120.1020.1120.1040.1150.1830.1100.1330.0850.1210.1190.0540.0910.0730.1640.0990.0360.0590.0190.0510.0370.0910.0740.0600.0360.0170.0330.006
ontological_synopsis0.2780.1450.1700.1630.1480.1060.0440.1150.0930.1900.0880.1540.0710.0840.1150.0480.1170.0730.1340.0820.0640.0880.0310.0570.0660.0400.0600.0660.0260.0200.0020.007
text-davinci-003narrative_synopsis0.3830.2320.2910.2350.2800.2330.1170.2610.2210.2850.1910.2570.1140.1930.2040.1000.2630.1030.2430.1380.1280.1510.0840.1370.1210.1260.1240.1430.0670.0280.0250.003
no_synopsis0.3500.2620.3160.2130.3380.2010.1750.2310.2170.3420.2350.2960.1630.2210.2470.1530.2170.1490.3220.1630.0820.1270.0380.1170.0910.2290.1790.1390.0870.0240.0260.008
ontological_synopsis0.3320.1420.1820.1430.1390.1420.0360.1480.1200.2210.1230.2050.0940.1140.1390.0900.1270.0870.1230.1030.0670.0770.0330.0680.0670.0380.0940.0800.0390.0160.0050.005
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 159, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -2180,7 +2327,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 17, "id": "9f53fd9c", "metadata": {}, "outputs": [ @@ -2188,21 +2335,21 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2217,115 +2364,115 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  goslim_genericanc_of_goslim_genericgoslim_agranc_of_goslim_agrclosure_of_goslim_genericclosure_of_goslim_agrgoslim_genericanc_of_goslim_genericgoslim_agranc_of_goslim_agrclosure_of_goslim_genericclosure_of_goslim_agr
model
N/Aclosure0.0310.0580.0190.0270.0890.046
random0.1070.0580.0730.0410.1660.114
rank_based0.2160.1340.1800.0910.3500.271
standard0.0490.1390.0420.0740.1890.116
standard_no_ontology0.0560.0240.0360.0140.0800.050
gpt-3.5-turbonarrative_synopsis0.2090.1220.0710.0660.3320.138
no_synopsis0.1600.1030.0300.0630.2630.093
ontological_synopsis0.1680.0720.0680.0520.2400.120
text-davinci-003narrative_synopsis0.2560.1500.1110.1160.4060.227
no_synopsis0.2410.2070.1270.1050.4470.232
ontological_synopsis0.1190.1220.0680.0880.2410.156N/Aclosure0.0270.0450.0150.0190.0720.034
random0.1000.0580.0750.0370.1580.112
rank_based0.1830.1290.1510.0790.3120.230
standard0.0480.1440.0360.0790.1920.115
standard_no_ontology0.0630.0350.0360.0130.0980.049
gpt-3.5-turbonarrative_synopsis0.2120.1120.0680.0600.3240.128
no_synopsis0.1810.1210.0590.0510.3020.110
ontological_synopsis0.1450.0840.0880.0570.2290.145
text-davinci-003narrative_synopsis0.2320.1930.1510.1370.4250.288
no_synopsis0.2620.2210.1270.1170.4830.243
ontological_synopsis0.1420.1140.0770.0680.2560.145
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 160, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2341,7 +2488,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 18, "id": "b6b4f797", "metadata": { "scrolled": true @@ -2351,21 +2498,21 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2380,118 +2527,119 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  goslim_genericanc_of_goslim_genericgoslim_agranc_of_goslim_agrclosure_of_goslim_genericclosure_of_goslim_agrgoslim_genericanc_of_goslim_genericgoslim_agranc_of_goslim_agrclosure_of_goslim_genericclosure_of_goslim_agr
model
text-davinci-003no_synopsis0.2410.2070.1270.1050.4470.232
narrative_synopsis0.2560.1500.1110.1160.4060.227
N/Arank_based0.2160.1340.1800.0910.3500.271
gpt-3.5-turbonarrative_synopsis0.2090.1220.0710.0660.3320.138
no_synopsis0.1600.1030.0300.0630.2630.093
text-davinci-003ontological_synopsis0.1190.1220.0680.0880.2410.156
gpt-3.5-turboontological_synopsis0.1680.0720.0680.0520.2400.120
N/Astandard0.0490.1390.0420.0740.1890.116
random0.1070.0580.0730.0410.1660.114
closure0.0310.0580.0190.0270.0890.046
standard_no_ontology0.0560.0240.0360.0140.0800.050text-davinci-003no_synopsis0.2620.2210.1270.1170.4830.243
narrative_synopsis0.2320.1930.1510.1370.4250.288
gpt-3.5-turbonarrative_synopsis0.2120.1120.0680.0600.3240.128
N/Arank_based0.1830.1290.1510.0790.3120.230
gpt-3.5-turbono_synopsis0.1810.1210.0590.0510.3020.110
text-davinci-003ontological_synopsis0.1420.1140.0770.0680.2560.145
gpt-3.5-turboontological_synopsis0.1450.0840.0880.0570.2290.145
N/Astandard0.0480.1440.0360.0790.1920.115
random0.1000.0580.0750.0370.1580.112
standard_no_ontology0.0630.0350.0360.0130.0980.049
closure0.0270.0450.0150.0190.0720.034
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 161, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2502,7 +2650,25 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 19, + "id": "6a587eee", + "metadata": {}, + "outputs": [], + "source": [ + "def agg_table(this_df, cols, exclude=[None]):\n", + " qcols = [MODEL, METHOD] + cols\n", + " agg_df = this_df[qcols].groupby([MODEL, METHOD]).mean(numeric_only=True)\n", + " for x in exclude:\n", + " agg_df= agg_df.query(f\"method != '{x}'\")\n", + " return agg_df.style.highlight_max(axis=0, props='font-weight:bold').format(precision=3)\n", + "\n", + "pd.options.display.precision = 2\n", + "pd.set_option(\"display.precision\", 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "id": "d59a8dfe", "metadata": {}, "outputs": [ @@ -2510,18 +2676,18 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2533,76 +2699,76 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  num GO termssize overlapnr size overlapnum GO termssize overlapnr size overlap
model
N/Aclosure3339.519161.5772.692N/Aclosure5015.814229.3642.214
random64.0193.8850.308random99.7217.6070.564
rank_based71.6928.2120.308rank_based113.71415.9500.814
standard_no_ontology46.48137.8465.288standard_no_ontology54.98645.7714.779
gpt-3.5-turbonarrative_synopsis3.7692.1150.462gpt-3.5-turbonarrative_synopsis4.0142.4070.521
no_synopsis5.7693.1150.558no_synopsis4.6073.0710.529
ontological_synopsis4.8082.8460.577ontological_synopsis3.9002.1860.471
text-davinci-003narrative_synopsis3.9811.3080.346text-davinci-003narrative_synopsis4.5931.5930.343
no_synopsis4.5581.6150.346no_synopsis3.5501.4640.293
ontological_synopsis6.7882.2500.462ontological_synopsis6.5861.8290.393
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 163, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2613,7 +2779,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 21, "id": "bd90d757", "metadata": {}, "outputs": [ @@ -2621,17 +2787,17 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2642,74 +2808,73 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  anc_of_goslim_genericanc_of_goslim_agranc_of_goslim_genericanc_of_goslim_agr
model
N/Astandard_no_ontology0.0240.014N/Astandard_no_ontology0.0350.013
closure0.0580.027closure0.0450.019
random0.0580.041random0.0580.037
gpt-3.5-turboontological_synopsis0.0720.052gpt-3.5-turbono_synopsis0.1210.051
no_synopsis0.1030.063ontological_synopsis0.0840.057
narrative_synopsis0.1220.066narrative_synopsis0.1120.060
N/Astandard0.1390.074text-davinci-003ontological_synopsis0.1140.068
text-davinci-003ontological_synopsis0.1220.088N/Astandard0.1440.079
N/Arank_based0.1340.091rank_based0.1290.079
text-davinci-003no_synopsis0.2070.105text-davinci-003no_synopsis0.2210.117
narrative_synopsis0.1500.116narrative_synopsis0.1930.137
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 164, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2720,7 +2885,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 22, "id": "18ccc356", "metadata": {}, "outputs": [], @@ -2752,55 +2917,37 @@ }, { "cell_type": "code", - "execution_count": 166, - "id": "6a587eee", - "metadata": {}, - "outputs": [], - "source": [ - "def agg_table(this_df, cols, exclude=[None]):\n", - " qcols = [MODEL, METHOD] + cols\n", - " agg_df = this_df[qcols].groupby([MODEL, METHOD]).mean(numeric_only=True)\n", - " for x in exclude:\n", - " agg_df= agg_df.query(f\"method != '{x}'\")\n", - " return agg_df.style.highlight_max(axis=0, props='font-weight:bold').format(precision=3)\n", - "\n", - "pd.options.display.precision = 2\n", - "pd.set_option(\"display.precision\", 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "id": "35fddeb4", + "execution_count": 23, + "id": "35fddeb4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2823,203 +2970,203 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  has top hitin top 5in top 10size overlapsimilaritynum termsnum GO termsnr size overlapnr similaritymean p valuemin p valuemax p valueproportion significantnum novelhas top hitin top 5in top 10size overlapsimilaritynum termsnum GO termsnr size overlapnr similaritymean p valuemin p valuemax p valueproportion significantnum novel
model
N/Aclosure1.0000.0000.000161.5770.0763388.4623339.5192.6920.0180.9180.0001.0000.0820.000
random0.0380.0190.0193.8850.01164.01964.0190.3080.0040.9600.3851.0000.04030.423
rank_based0.0380.0000.0008.2120.02471.69271.6920.3080.0090.9150.2511.0000.0867.385
standard1.0001.0001.000161.5770.970167.731161.57714.6730.9740.0080.0000.0461.0000.000
standard_no_ontology0.5960.4620.48137.8460.24446.48146.4815.2880.2050.2320.0000.9810.7730.000
gpt-3.5-turbonarrative_synopsis0.1540.1540.1542.1150.0185.4043.7690.4620.0350.4380.1710.7450.5640.212
no_synopsis0.1540.1350.1543.1150.0277.2885.7690.5580.0330.4280.1030.7360.5730.481
ontological_synopsis0.2690.2500.2692.8460.0316.2124.8080.5770.0390.3860.1160.6750.6150.077
text-davinci-003narrative_synopsis0.0770.0770.0771.3080.01010.6353.9810.3460.0210.6420.2840.8910.3600.327
no_synopsis0.1350.1150.1351.6150.0098.8464.5580.3460.0170.6500.3460.8850.3510.673
ontological_synopsis0.1350.0770.1352.2500.01811.4426.7880.4620.0240.6460.2320.9430.3550.442N/Aclosure1.0000.0140.014229.3640.0685078.1645015.8142.2140.0090.9290.0001.0000.0710.000
random0.0430.0070.0077.6070.01999.72199.7210.5640.0080.9340.1221.0000.06739.329
rank_based0.1570.0140.01415.9500.038113.714113.7140.8140.0140.8770.0871.0000.1247.543
standard1.0001.0001.000229.3640.966236.457229.36417.8430.9650.0070.0000.0471.0000.000
standard_no_ontology0.5070.3790.40745.7710.19354.98654.9864.7790.1400.2180.0001.0000.7870.000
gpt-3.5-turbonarrative_synopsis0.1500.1430.1502.4070.0145.7214.0140.5210.0260.3820.1350.6720.6190.193
no_synopsis0.1500.1430.1433.0710.0185.5214.6070.5290.0290.3450.0890.6690.6570.229
ontological_synopsis0.1570.1500.1572.1860.0165.9793.9000.4710.0300.4370.1970.7180.5640.093
text-davinci-003narrative_synopsis0.0710.0640.0711.5930.00912.6794.5930.3430.0170.6210.2450.9140.3800.364
no_synopsis0.0640.0570.0641.4640.00811.6863.5500.2930.0130.5550.2610.8220.4470.350
ontological_synopsis0.1210.0500.0931.8290.01113.1576.5860.3930.0180.6950.3030.9710.3060.279
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 167, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -3031,7 +3178,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 24, "id": "8c00964a", "metadata": {}, "outputs": [ @@ -3039,19 +3186,19 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3064,77 +3211,69 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  proportion significantnum GO termshas top hitnum novelproportion significantnum GO termshas top hitnum novel
model
N/Aclosure0.0823339.5191.0000.000
gpt-3.5-turbonarrative_synopsis0.5643.7690.1540.212gpt-3.5-turbonarrative_synopsis0.6194.0140.1500.193
no_synopsis0.5735.7690.1540.481no_synopsis0.6574.6070.1500.229
ontological_synopsis0.6154.8080.2690.077ontological_synopsis0.5643.9000.1570.093
text-davinci-003narrative_synopsis0.3603.9810.0770.327text-davinci-003narrative_synopsis0.3804.5930.0710.364
no_synopsis0.3514.5580.1350.673no_synopsis0.4473.5500.0640.350
ontological_synopsis0.3556.7880.1350.442ontological_synopsis0.3066.5860.1210.279
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 168, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "agg_table(df, [PROPOTION_SIGNIFICANT, NUM_GO_TERMS, HAS_TOP_HIT, NUM_NOVEL], [\"standard\", \"standard_no_ontology\", \"random\", \"rank_based\"])\n", + "agg_table(df, [PROPOTION_SIGNIFICANT, NUM_GO_TERMS, HAS_TOP_HIT, NUM_NOVEL], [\"standard\", \"standard_no_ontology\", \"random\", \"rank_based\", \"closure\"])\n", "#means.query(\"method != 'standard'\").style.highlight_max(axis=0, props='font-weight:bold').format(precision=3)" ] }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 25, "id": "c7487343", "metadata": {}, "outputs": [ @@ -3142,19 +3281,19 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3167,57 +3306,57 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  proportion significantnum GO termshas top hitnum novelproportion significantnum GO termshas top hitnum novel
model
gpt-3.5-turbonarrative_synopsis0.5673.3460.1540.269gpt-3.5-turbonarrative_synopsis0.6173.8710.1000.171
no_synopsis0.5656.1150.1150.462no_synopsis0.6694.6140.1290.257
ontological_synopsis0.6454.4620.1920.038ontological_synopsis0.5904.2000.1710.071
text-davinci-003narrative_synopsis0.4024.2310.0770.385text-davinci-003narrative_synopsis0.3824.6000.0570.343
no_synopsis0.3724.6150.1540.769no_synopsis0.4843.6140.0710.329
ontological_synopsis0.3536.9620.1150.462ontological_synopsis0.3115.9430.1000.271
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 171, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -3237,7 +3376,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "id": "bd936323", "metadata": {}, "outputs": [ @@ -3245,29 +3384,29 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3290,186 +3429,203 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  has top hitin top 5in top 10size overlapsimilaritynum termsnum GO termsnr size overlapnr similaritymean p valuemin p valuemax p valueproportion significantnum novelhas top hitin top 5in top 10size overlapsimilaritynum termsnum GO termsnr size overlapnr similaritymean p valuemin p valuemax p valueproportion significantnum novel
model
N/ArandomTrueTrueTrue270.04618218220.0671.0001.0001.0000.16375
rank_basedTrueFalseFalse500.09020020020.0481.0001.0001.0000.33353
standardTrueTrueTrue4911.000491491331.0000.0240.0000.0501.0000
standard_no_ontologyTrueTrueTrue1010.449113113110.8000.5410.0001.0001.0000
gpt-3.5-turbonarrative_synopsisTrueTrueTrue70.143141130.3331.0001.0001.0001.0002
no_synopsisTrueTrueTrue90.125401930.2501.0001.0001.0001.0003
ontological_synopsisTrueTrueTrue90.182111020.2861.0001.0001.0001.0001
text-davinci-003narrative_synopsisTrueTrueTrue70.044281220.1821.0001.0001.0001.0003
no_synopsisTrueTrueTrue90.060201230.1581.0001.0001.0001.0004
ontological_synopsisTrueTrueTrue90.116282030.1881.0001.0001.0001.0003N/AclosureTrueTrueTrue8720.23590568977120.1350.9960.0021.0000.2610
randomTrueTrueTrue350.08318218230.1111.0001.0001.0000.25087
rank_basedTrueTrueTrue580.09020020060.1821.0001.0001.0000.34353
standardTrueTrueTrue8721.000873872401.0000.0240.0020.0501.0000
standard_no_ontologyTrueTrueTrue1990.449227227180.8000.6690.0251.0000.9690
gpt-3.5-turbonarrative_synopsisTrueTrueTrue80.100171140.2001.0001.0001.0001.0003
no_synopsisTrueTrueTrue110.167141230.2501.0001.0001.0001.0003
ontological_synopsisTrueTrueTrue80.222171330.6671.0001.0001.0001.0001
text-davinci-003narrative_synopsisTrueTrueTrue70.083341720.1251.0001.0001.0001.0003
no_synopsisTrueTrueTrue70.078611120.1181.0001.0001.0001.0004
ontological_synopsisTrueTrueTrue70.130312140.1821.0001.0001.0001.0002
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -3491,13 +3647,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "id": "6f79eece", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -3519,7 +3675,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "id": "f2e1f5ce", "metadata": {}, "outputs": [ @@ -3529,13 +3685,13 @@ "" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -3554,7 +3710,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "id": "324b3c1d", "metadata": {}, "outputs": [ @@ -3592,64 +3748,64 @@ " FA\n", " \n", " \n", - " 88\n", - " HALLMARK_HEDGEHOG_SIGNALING\n", + " 110\n", + " HALLMARK_ANGIOGENESIS\n", " \n", " \n", - " 176\n", - " Yamanaka-TFs\n", + " 154\n", + " HALLMARK_APICAL_SURFACE\n", " \n", " \n", - " 198\n", - " amigo-example\n", + " 462\n", + " HALLMARK_HEDGEHOG_SIGNALING\n", " \n", " \n", " ...\n", " ...\n", " \n", " \n", - " 418\n", + " 1386\n", " molecular sequestering\n", " \n", " \n", - " 462\n", + " 1430\n", " peroxisome\n", " \n", " \n", - " 484\n", + " 1452\n", " progeria\n", " \n", " \n", - " 506\n", + " 1474\n", " regulation of presynaptic membrane potential\n", " \n", " \n", - " 528\n", + " 1496\n", " sensory ataxia\n", " \n", " \n", "\n", - "

14 rows × 1 columns

\n", + "

21 rows × 1 columns

\n", "" ], "text/plain": [ - " source geneset\n", - "0 EDS\n", - "22 FA\n", - "88 HALLMARK_HEDGEHOG_SIGNALING\n", - "176 Yamanaka-TFs\n", - "198 amigo-example\n", - ".. ...\n", - "418 molecular sequestering\n", - "462 peroxisome\n", - "484 progeria\n", - "506 regulation of presynaptic membrane potential\n", - "528 sensory ataxia\n", + " source geneset\n", + "0 EDS\n", + "22 FA\n", + "110 HALLMARK_ANGIOGENESIS\n", + "154 HALLMARK_APICAL_SURFACE\n", + "462 HALLMARK_HEDGEHOG_SIGNALING\n", + "... ...\n", + "1386 molecular sequestering\n", + "1430 peroxisome\n", + "1452 progeria\n", + "1474 regulation of presynaptic membrane potential\n", + "1496 sensory ataxia\n", "\n", - "[14 rows x 1 columns]" + "[21 rows x 1 columns]" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -3660,7 +3816,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "id": "2ab6ac9f", "metadata": {}, "outputs": [ @@ -3722,53 +3878,70 @@ " \n", " \n", " \n", - " N/A\n", + " N/A\n", + " closure\n", + " 1.00\n", + " 0.00\n", + " 0.00\n", + " 126.12\n", + " 7.58e-02\n", + " 2422.12\n", + " 2378.76\n", + " 3.31\n", + " 0.02\n", + " 9.19e-01\n", + " 4.59e-05\n", + " 1.00\n", + " 0.08\n", + " 0.00\n", + " \n", + " \n", " random\n", " 0.00\n", " 0.00\n", " 0.00\n", - " 0.64\n", - " 3.79e-03\n", - " 20.43\n", - " 20.43\n", - " 0.07\n", - " 2.38e-03\n", - " 9.77e-01\n", - " 6.43e-01\n", + " 1.07\n", + " 8.70e-03\n", + " 25.07\n", + " 25.07\n", + " 0.38\n", + " 0.01\n", + " 9.55e-01\n", + " 3.60e-01\n", " 1.00\n", - " 0.02\n", - " 12.71\n", + " 0.05\n", + " 13.76\n", " \n", " \n", " rank_based\n", + " 0.05\n", " 0.00\n", " 0.00\n", - " 0.00\n", - " 1.43\n", - " 8.44e-03\n", - " 21.07\n", - " 21.07\n", - " 0.21\n", - " 8.31e-03\n", - " 9.47e-01\n", - " 3.95e-01\n", + " 2.57\n", + " 1.68e-02\n", + " 26.10\n", + " 26.10\n", + " 0.40\n", + " 0.02\n", + " 9.17e-01\n", + " 2.41e-01\n", " 1.00\n", - " 0.05\n", - " 2.11\n", + " 0.08\n", + " 2.17\n", " \n", " \n", " standard\n", " 1.00\n", " 1.00\n", " 1.00\n", - " 117.54\n", + " 126.12\n", " 9.83e-01\n", - " 120.82\n", - " 117.54\n", - " 14.14\n", - " 9.73e-01\n", - " 9.09e-03\n", - " 5.83e-06\n", + " 129.12\n", + " 126.12\n", + " 16.29\n", + " 0.98\n", + " 9.45e-03\n", + " 4.59e-05\n", " 0.05\n", " 1.00\n", " 0.00\n", @@ -3776,209 +3949,219 @@ " \n", " standard_no_ontology\n", " 0.64\n", - " 0.54\n", - " 0.54\n", - " 29.11\n", - " 2.53e-01\n", - " 37.89\n", - " 37.89\n", - " 5.11\n", - " 2.09e-01\n", - " 2.75e-01\n", - " 2.89e-05\n", - " 0.97\n", - " 0.73\n", + " 0.57\n", + " 0.57\n", + " 26.93\n", + " 2.15e-01\n", + " 36.05\n", + " 36.05\n", + " 5.81\n", + " 0.21\n", + " 2.85e-01\n", + " 6.19e-04\n", + " 1.00\n", + " 0.72\n", " 0.00\n", " \n", " \n", - " gpt-3.5-turbo\n", - " narrative_synopsis\n", - " 0.18\n", - " 0.18\n", - " 0.18\n", - " 1.50\n", - " 1.92e-02\n", - " 5.25\n", - " 3.00\n", - " 0.43\n", - " 3.82e-02\n", - " 5.04e-01\n", - " 2.62e-01\n", - " 0.70\n", - " 0.50\n", - " 0.18\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", + " gpt-3.5-turbo\n", " no_synopsis\n", - " 0.18\n", - " 0.14\n", - " 0.18\n", - " 2.25\n", - " 2.97e-02\n", - " 6.07\n", - " 5.18\n", - " 0.54\n", - " 3.63e-02\n", - " 4.74e-01\n", - " 1.17e-01\n", + " 0.21\n", + " 0.21\n", + " 0.21\n", + " 2.10\n", + " 2.48e-02\n", + " 4.88\n", + " 4.07\n", " 0.69\n", + " 0.04\n", + " 4.73e-01\n", + " 1.47e-01\n", + " 0.85\n", " 0.53\n", - " 0.57\n", + " 0.21\n", " \n", " \n", " ontological_synopsis\n", - " 0.39\n", - " 0.39\n", - " 0.39\n", - " 2.82\n", - " 4.49e-02\n", - " 5.79\n", - " 4.86\n", - " 0.75\n", - " 5.51e-02\n", + " 0.45\n", + " 0.43\n", + " 0.45\n", + " 2.74\n", + " 3.29e-02\n", + " 5.86\n", + " 4.76\n", + " 0.93\n", + " 0.07\n", " 3.89e-01\n", - " 7.30e-02\n", - " 0.68\n", + " 9.69e-02\n", + " 0.74\n", " 0.61\n", - " 0.07\n", + " 0.10\n", " \n", " \n", " text-davinci-003\n", " narrative_synopsis\n", - " 0.07\n", - " 0.07\n", - " 0.07\n", - " 0.89\n", - " 8.48e-03\n", - " 8.54\n", - " 2.68\n", - " 0.32\n", - " 1.75e-02\n", - " 6.52e-01\n", - " 3.76e-01\n", + " 0.10\n", + " 0.10\n", + " 0.10\n", + " 1.36\n", + " 1.25e-02\n", + " 9.55\n", + " 3.17\n", + " 0.38\n", + " 0.02\n", + " 5.81e-01\n", + " 3.44e-01\n", " 0.83\n", - " 0.35\n", - " 0.21\n", + " 0.42\n", + " 0.26\n", " \n", " \n", " no_synopsis\n", - " 0.14\n", - " 0.14\n", - " 0.14\n", - " 0.96\n", - " 7.69e-03\n", - " 7.96\n", - " 3.96\n", - " 0.32\n", - " 1.44e-02\n", - " 7.33e-01\n", - " 4.64e-01\n", - " 0.93\n", - " 0.27\n", - " 0.96\n", + " 0.07\n", + " 0.07\n", + " 0.07\n", + " 1.07\n", + " 1.10e-02\n", + " 10.45\n", + " 3.00\n", + " 0.19\n", + " 0.01\n", + " 5.78e-01\n", + " 3.43e-01\n", + " 0.79\n", + " 0.42\n", + " 0.38\n", " \n", " \n", " ontological_synopsis\n", - " 0.25\n", + " 0.29\n", " 0.14\n", - " 0.25\n", - " 2.50\n", - " 2.40e-02\n", - " 10.57\n", - " 7.43\n", - " 0.54\n", - " 2.82e-02\n", - " 6.43e-01\n", - " 2.16e-01\n", - " 1.00\n", - " 0.36\n", - " 0.68\n", + " 0.24\n", + " 2.02\n", + " 1.99e-02\n", + " 11.55\n", + " 7.19\n", + " 0.74\n", + " 0.04\n", + " 6.71e-01\n", + " 2.70e-01\n", + " 0.95\n", + " 0.33\n", + " 0.21\n", " \n", " \n", "\n", + "

11 rows × 14 columns

\n", "" ], "text/plain": [ " has top hit in top 5 in top 10 \\\n", "model method \n", - "N/A random 0.00 0.00 0.00 \n", - " rank_based 0.00 0.00 0.00 \n", + "N/A closure 1.00 0.00 0.00 \n", + " random 0.00 0.00 0.00 \n", + " rank_based 0.05 0.00 0.00 \n", " standard 1.00 1.00 1.00 \n", - " standard_no_ontology 0.64 0.54 0.54 \n", - "gpt-3.5-turbo narrative_synopsis 0.18 0.18 0.18 \n", - " no_synopsis 0.18 0.14 0.18 \n", - " ontological_synopsis 0.39 0.39 0.39 \n", - "text-davinci-003 narrative_synopsis 0.07 0.07 0.07 \n", - " no_synopsis 0.14 0.14 0.14 \n", - " ontological_synopsis 0.25 0.14 0.25 \n", + " standard_no_ontology 0.64 0.57 0.57 \n", + "... ... ... ... \n", + "gpt-3.5-turbo no_synopsis 0.21 0.21 0.21 \n", + " ontological_synopsis 0.45 0.43 0.45 \n", + "text-davinci-003 narrative_synopsis 0.10 0.10 0.10 \n", + " no_synopsis 0.07 0.07 0.07 \n", + " ontological_synopsis 0.29 0.14 0.24 \n", "\n", " size overlap similarity num terms \\\n", "model method \n", - "N/A random 0.64 3.79e-03 20.43 \n", - " rank_based 1.43 8.44e-03 21.07 \n", - " standard 117.54 9.83e-01 120.82 \n", - " standard_no_ontology 29.11 2.53e-01 37.89 \n", - "gpt-3.5-turbo narrative_synopsis 1.50 1.92e-02 5.25 \n", - " no_synopsis 2.25 2.97e-02 6.07 \n", - " ontological_synopsis 2.82 4.49e-02 5.79 \n", - "text-davinci-003 narrative_synopsis 0.89 8.48e-03 8.54 \n", - " no_synopsis 0.96 7.69e-03 7.96 \n", - " ontological_synopsis 2.50 2.40e-02 10.57 \n", + "N/A closure 126.12 7.58e-02 2422.12 \n", + " random 1.07 8.70e-03 25.07 \n", + " rank_based 2.57 1.68e-02 26.10 \n", + " standard 126.12 9.83e-01 129.12 \n", + " standard_no_ontology 26.93 2.15e-01 36.05 \n", + "... ... ... ... \n", + "gpt-3.5-turbo no_synopsis 2.10 2.48e-02 4.88 \n", + " ontological_synopsis 2.74 3.29e-02 5.86 \n", + "text-davinci-003 narrative_synopsis 1.36 1.25e-02 9.55 \n", + " no_synopsis 1.07 1.10e-02 10.45 \n", + " ontological_synopsis 2.02 1.99e-02 11.55 \n", "\n", " num GO terms nr size overlap \\\n", "model method \n", - "N/A random 20.43 0.07 \n", - " rank_based 21.07 0.21 \n", - " standard 117.54 14.14 \n", - " standard_no_ontology 37.89 5.11 \n", - "gpt-3.5-turbo narrative_synopsis 3.00 0.43 \n", - " no_synopsis 5.18 0.54 \n", - " ontological_synopsis 4.86 0.75 \n", - "text-davinci-003 narrative_synopsis 2.68 0.32 \n", - " no_synopsis 3.96 0.32 \n", - " ontological_synopsis 7.43 0.54 \n", + "N/A closure 2378.76 3.31 \n", + " random 25.07 0.38 \n", + " rank_based 26.10 0.40 \n", + " standard 126.12 16.29 \n", + " standard_no_ontology 36.05 5.81 \n", + "... ... ... \n", + "gpt-3.5-turbo no_synopsis 4.07 0.69 \n", + " ontological_synopsis 4.76 0.93 \n", + "text-davinci-003 narrative_synopsis 3.17 0.38 \n", + " no_synopsis 3.00 0.19 \n", + " ontological_synopsis 7.19 0.74 \n", "\n", " nr similarity mean p value \\\n", "model method \n", - "N/A random 2.38e-03 9.77e-01 \n", - " rank_based 8.31e-03 9.47e-01 \n", - " standard 9.73e-01 9.09e-03 \n", - " standard_no_ontology 2.09e-01 2.75e-01 \n", - "gpt-3.5-turbo narrative_synopsis 3.82e-02 5.04e-01 \n", - " no_synopsis 3.63e-02 4.74e-01 \n", - " ontological_synopsis 5.51e-02 3.89e-01 \n", - "text-davinci-003 narrative_synopsis 1.75e-02 6.52e-01 \n", - " no_synopsis 1.44e-02 7.33e-01 \n", - " ontological_synopsis 2.82e-02 6.43e-01 \n", + "N/A closure 0.02 9.19e-01 \n", + " random 0.01 9.55e-01 \n", + " rank_based 0.02 9.17e-01 \n", + " standard 0.98 9.45e-03 \n", + " standard_no_ontology 0.21 2.85e-01 \n", + "... ... ... \n", + "gpt-3.5-turbo no_synopsis 0.04 4.73e-01 \n", + " ontological_synopsis 0.07 3.89e-01 \n", + "text-davinci-003 narrative_synopsis 0.02 5.81e-01 \n", + " no_synopsis 0.01 5.78e-01 \n", + " ontological_synopsis 0.04 6.71e-01 \n", "\n", " min p value max p value \\\n", "model method \n", - "N/A random 6.43e-01 1.00 \n", - " rank_based 3.95e-01 1.00 \n", - " standard 5.83e-06 0.05 \n", - " standard_no_ontology 2.89e-05 0.97 \n", - "gpt-3.5-turbo narrative_synopsis 2.62e-01 0.70 \n", - " no_synopsis 1.17e-01 0.69 \n", - " ontological_synopsis 7.30e-02 0.68 \n", - "text-davinci-003 narrative_synopsis 3.76e-01 0.83 \n", - " no_synopsis 4.64e-01 0.93 \n", - " ontological_synopsis 2.16e-01 1.00 \n", + "N/A closure 4.59e-05 1.00 \n", + " random 3.60e-01 1.00 \n", + " rank_based 2.41e-01 1.00 \n", + " standard 4.59e-05 0.05 \n", + " standard_no_ontology 6.19e-04 1.00 \n", + "... ... ... \n", + "gpt-3.5-turbo no_synopsis 1.47e-01 0.85 \n", + " ontological_synopsis 9.69e-02 0.74 \n", + "text-davinci-003 narrative_synopsis 3.44e-01 0.83 \n", + " no_synopsis 3.43e-01 0.79 \n", + " ontological_synopsis 2.70e-01 0.95 \n", "\n", " proportion significant num novel \n", "model method \n", - "N/A random 0.02 12.71 \n", - " rank_based 0.05 2.11 \n", + "N/A closure 0.08 0.00 \n", + " random 0.05 13.76 \n", + " rank_based 0.08 2.17 \n", " standard 1.00 0.00 \n", - " standard_no_ontology 0.73 0.00 \n", - "gpt-3.5-turbo narrative_synopsis 0.50 0.18 \n", - " no_synopsis 0.53 0.57 \n", - " ontological_synopsis 0.61 0.07 \n", - "text-davinci-003 narrative_synopsis 0.35 0.21 \n", - " no_synopsis 0.27 0.96 \n", - " ontological_synopsis 0.36 0.68 " + " standard_no_ontology 0.72 0.00 \n", + "... ... ... \n", + "gpt-3.5-turbo no_synopsis 0.53 0.21 \n", + " ontological_synopsis 0.61 0.10 \n", + "text-davinci-003 narrative_synopsis 0.42 0.26 \n", + " no_synopsis 0.42 0.38 \n", + " ontological_synopsis 0.33 0.21 \n", + "\n", + "[11 rows x 14 columns]" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -3991,7 +4174,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "id": "7ec9701c", "metadata": {}, "outputs": [ @@ -3999,29 +4182,29 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4044,169 +4227,186 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  has top hitin top 5in top 10size overlapsimilaritynum termsnum GO termsnr size overlapnr similaritymean p valuemin p valuemax p valueproportion significantnum novelhas top hitin top 5in top 10size overlapsimilaritynum termsnum GO termsnr size overlapnr similaritymean p valuemin p valuemax p valueproportion significantnum novel
model
N/Arandom0.0000.0000.0000.6430.00420.42920.4290.0710.0020.9770.6431.0000.02312.714
rank_based0.0000.0000.0001.4290.00821.07121.0710.2140.0080.9470.3951.0000.0542.107
standard_no_ontology0.6430.5360.53629.1070.25337.89337.8935.1070.2090.2750.0000.9650.7300.000
gpt-3.5-turbonarrative_synopsis0.1790.1790.1791.5000.0195.2503.0000.4290.0380.5040.2620.6970.4980.179
no_synopsis0.1790.1430.1792.2500.0306.0715.1790.5360.0360.4740.1170.6940.5280.571
ontological_synopsis0.3930.3930.3932.8210.0455.7864.8570.7500.0550.3890.0730.6810.6130.071
text-davinci-003narrative_synopsis0.0710.0710.0710.8930.0088.5362.6790.3210.0170.6520.3760.8330.3490.214
no_synopsis0.1430.1430.1430.9640.0087.9643.9640.3210.0140.7330.4640.9290.2670.964
ontological_synopsis0.2500.1430.2502.5000.02410.5717.4290.5360.0280.6430.2161.0000.3570.679N/Aclosure1.0000.0000.000126.1190.0762422.1192378.7623.3100.0210.9190.0001.0000.0810.000
random0.0000.0000.0001.0710.00925.07125.0710.3810.0130.9550.3601.0000.04513.762
rank_based0.0480.0000.0002.5710.01726.09526.0950.4050.0180.9170.2411.0000.0842.167
standard_no_ontology0.6430.5710.57126.9290.21536.04836.0485.8100.2110.2850.0011.0000.7220.000
gpt-3.5-turbonarrative_synopsis0.1900.1900.1901.8100.0195.5483.5240.5480.0290.5160.2450.8120.4860.190
no_synopsis0.2140.2140.2142.0950.0254.8814.0710.6900.0440.4730.1470.8550.5300.214
ontological_synopsis0.4520.4290.4522.7380.0335.8574.7620.9290.0710.3890.0970.7390.6130.095
text-davinci-003narrative_synopsis0.0950.0950.0951.3570.0139.5483.1670.3810.0220.5810.3440.8300.4210.262
no_synopsis0.0710.0710.0711.0710.01110.4523.0000.1900.0110.5780.3430.7900.4240.381
ontological_synopsis0.2860.1430.2382.0240.02011.5487.1900.7380.0370.6710.2700.9510.3300.214
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -4217,7 +4417,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "id": "ec20a512", "metadata": {}, "outputs": [ @@ -4225,19 +4425,19 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4250,63 +4450,63 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
  proportion significantnum GO termshas top hitnum novelproportion significantnum GO termshas top hitnum novel
model
gpt-3.5-turbonarrative_synopsis0.4983.0000.1790.179gpt-3.5-turbonarrative_synopsis0.4863.5240.1900.190
no_synopsis0.5285.1790.1790.571no_synopsis0.5304.0710.2140.214
ontological_synopsis0.6134.8570.3930.071ontological_synopsis0.6134.7620.4520.095
text-davinci-003narrative_synopsis0.3492.6790.0710.214text-davinci-003narrative_synopsis0.4213.1670.0950.262
no_synopsis0.2673.9640.1430.964no_synopsis0.4243.0000.0710.381
ontological_synopsis0.3577.4290.2500.679ontological_synopsis0.3307.1900.2860.214
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "agg_table(df_small, [PROPOTION_SIGNIFICANT, NUM_GO_TERMS, HAS_TOP_HIT, NUM_NOVEL], [\"standard\", \"standard_no_ontology\", \"random\", \"rank_based\"])" + "agg_table(df_small, [PROPOTION_SIGNIFICANT, NUM_GO_TERMS, HAS_TOP_HIT, NUM_NOVEL], [\"closure\", \"standard\", \"standard_no_ontology\", \"random\", \"rank_based\"])" ] }, { @@ -4319,7 +4519,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 33, "id": "f97cd9a4", "metadata": {}, "outputs": [], @@ -4370,7 +4570,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 34, "id": "7d7f78b2", "metadata": {}, "outputs": [ @@ -4415,7 +4615,7 @@ " \n", " \n", " \n", - " 468\n", + " 1436\n", " N/A\n", " standard\n", " True\n", @@ -4434,7 +4634,7 @@ " 0\n", " \n", " \n", - " 469\n", + " 1437\n", " N/A\n", " standard_no_ontology\n", " True\n", @@ -4453,9 +4653,9 @@ " 0\n", " \n", " \n", - " 472\n", + " 1440\n", " N/A\n", - " None\n", + " closure\n", " True\n", " False\n", " False\n", @@ -4463,8 +4663,8 @@ " 0.19\n", " 419\n", " 391\n", - " 6\n", - " 0.12\n", + " 7\n", + " 0.13\n", " 8.02e-01\n", " 1.98e-16\n", " 1.00e+00\n", @@ -4472,72 +4672,72 @@ " 0\n", " \n", " \n", - " 466\n", - " text-davinci-003\n", + " 1431\n", + " gpt-3.5-turbo\n", " ontological_synopsis\n", " True\n", " True\n", " True\n", - " 3\n", - " 0.04\n", - " 10\n", - " 9\n", - " 0\n", - " 0.00\n", - " 6.70e-01\n", + " 4\n", + " 0.06\n", + " 6\n", + " 4\n", + " 2\n", + " 0.18\n", + " 1.08e-04\n", " 1.98e-16\n", - " 1.00e+00\n", - " 0.33\n", - " 3\n", + " 4.30e-04\n", + " 1.00\n", + " 0\n", " \n", " \n", - " 462\n", + " 1430\n", " gpt-3.5-turbo\n", " no_synopsis\n", " False\n", " False\n", " False\n", - " 1\n", - " 0.02\n", + " 2\n", + " 0.03\n", + " 6\n", " 3\n", " 1\n", - " 0\n", - " 0.00\n", - " 6.88e-15\n", + " 0.09\n", + " 3.33e-01\n", " 6.88e-15\n", - " 6.88e-15\n", - " 1.00\n", - " 0\n", + " 1.00e+00\n", + " 0.67\n", + " 1\n", " \n", " \n", - " 463\n", - " gpt-3.5-turbo\n", - " ontological_synopsis\n", - " False\n", - " False\n", - " False\n", - " 1\n", - " 0.02\n", - " 5\n", - " 1\n", - " 0\n", - " 0.00\n", - " 6.88e-15\n", - " 6.88e-15\n", - " 6.88e-15\n", - " 1.00\n", - " 0\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 464\n", - " gpt-3.5-turbo\n", - " narrative_synopsis\n", + " 1433\n", + " text-davinci-003\n", + " no_synopsis\n", " False\n", " False\n", " False\n", " 0\n", " 0.00\n", - " 4\n", + " 6\n", " 0\n", " 0\n", " 0.00\n", @@ -4548,26 +4748,26 @@ " 0\n", " \n", " \n", - " 465\n", + " 1434\n", " text-davinci-003\n", - " no_synopsis\n", + " ontological_synopsis\n", " False\n", " False\n", " False\n", " 0\n", " 0.00\n", - " 9\n", - " 2\n", + " 1\n", + " 0\n", " 0\n", " 0.00\n", - " 1.00e+00\n", - " 1.00e+00\n", - " 1.00e+00\n", - " 0.00\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " 0\n", " \n", " \n", - " 467\n", + " 1435\n", " text-davinci-003\n", " narrative_synopsis\n", " False\n", @@ -4575,18 +4775,18 @@ " False\n", " 0\n", " 0.00\n", - " 4\n", - " 0\n", + " 5\n", + " 1\n", " 0\n", " 0.00\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 0\n", + " 1.00e+00\n", + " 1.00e+00\n", + " 1.00e+00\n", + " 0.00\n", + " 1\n", " \n", " \n", - " 470\n", + " 1438\n", " N/A\n", " random\n", " False\n", @@ -4602,10 +4802,10 @@ " 1.00e+00\n", " 1.00e+00\n", " 0.00\n", - " 8\n", + " 7\n", " \n", " \n", - " 471\n", + " 1439\n", " N/A\n", " rank_based\n", " False\n", @@ -4625,63 +4825,66 @@ " \n", " \n", "\n", + "

11 rows × 16 columns

\n", "" ], "text/plain": [ - " model method has top hit in top 5 in top 10 \\\n", - "468 N/A standard True True True \n", - "469 N/A standard_no_ontology True False False \n", - "472 N/A None True False False \n", - "466 text-davinci-003 ontological_synopsis True True True \n", - "462 gpt-3.5-turbo no_synopsis False False False \n", - "463 gpt-3.5-turbo ontological_synopsis False False False \n", - "464 gpt-3.5-turbo narrative_synopsis False False False \n", - "465 text-davinci-003 no_synopsis False False False \n", - "467 text-davinci-003 narrative_synopsis False False False \n", - "470 N/A random False False False \n", - "471 N/A rank_based False False False \n", + " model method has top hit in top 5 \\\n", + "1436 N/A standard True True \n", + "1437 N/A standard_no_ontology True False \n", + "1440 N/A closure True False \n", + "1431 gpt-3.5-turbo ontological_synopsis True True \n", + "1430 gpt-3.5-turbo no_synopsis False False \n", + "... ... ... ... ... \n", + "1433 text-davinci-003 no_synopsis False False \n", + "1434 text-davinci-003 ontological_synopsis False False \n", + "1435 text-davinci-003 narrative_synopsis False False \n", + "1438 N/A random False False \n", + "1439 N/A rank_based False False \n", "\n", - " size overlap similarity num terms num GO terms nr size overlap \\\n", - "468 62 1.00 62 62 10 \n", - "469 15 0.22 21 21 7 \n", - "472 62 0.19 419 391 6 \n", - "466 3 0.04 10 9 0 \n", - "462 1 0.02 3 1 0 \n", - "463 1 0.02 5 1 0 \n", - "464 0 0.00 4 0 0 \n", - "465 0 0.00 9 2 0 \n", - "467 0 0.00 4 0 0 \n", - "470 0 0.00 8 8 0 \n", - "471 0 0.00 8 8 0 \n", + " in top 10 size overlap similarity num terms num GO terms \\\n", + "1436 True 62 1.00 62 62 \n", + "1437 False 15 0.22 21 21 \n", + "1440 False 62 0.19 419 391 \n", + "1431 True 4 0.06 6 4 \n", + "1430 False 2 0.03 6 3 \n", + "... ... ... ... ... ... \n", + "1433 False 0 0.00 6 0 \n", + "1434 False 0 0.00 1 0 \n", + "1435 False 0 0.00 5 1 \n", + "1438 False 0 0.00 8 8 \n", + "1439 False 0 0.00 8 8 \n", "\n", - " nr similarity mean p value min p value max p value \\\n", - "468 1.00 5.64e-03 1.98e-16 4.11e-02 \n", - "469 0.41 2.89e-01 1.98e-16 1.00e+00 \n", - "472 0.12 8.02e-01 1.98e-16 1.00e+00 \n", - "466 0.00 6.70e-01 1.98e-16 1.00e+00 \n", - "462 0.00 6.88e-15 6.88e-15 6.88e-15 \n", - "463 0.00 6.88e-15 6.88e-15 6.88e-15 \n", - "464 0.00 NaN NaN NaN \n", - "465 0.00 1.00e+00 1.00e+00 1.00e+00 \n", - "467 0.00 NaN NaN NaN \n", - "470 0.00 1.00e+00 1.00e+00 1.00e+00 \n", - "471 0.00 1.00e+00 1.00e+00 1.00e+00 \n", + " nr size overlap nr similarity mean p value min p value max p value \\\n", + "1436 10 1.00 5.64e-03 1.98e-16 4.11e-02 \n", + "1437 7 0.41 2.89e-01 1.98e-16 1.00e+00 \n", + "1440 7 0.13 8.02e-01 1.98e-16 1.00e+00 \n", + "1431 2 0.18 1.08e-04 1.98e-16 4.30e-04 \n", + "1430 1 0.09 3.33e-01 6.88e-15 1.00e+00 \n", + "... ... ... ... ... ... \n", + "1433 0 0.00 NaN NaN NaN \n", + "1434 0 0.00 NaN NaN NaN \n", + "1435 0 0.00 1.00e+00 1.00e+00 1.00e+00 \n", + "1438 0 0.00 1.00e+00 1.00e+00 1.00e+00 \n", + "1439 0 0.00 1.00e+00 1.00e+00 1.00e+00 \n", "\n", - " proportion significant num novel \n", - "468 1.00 0 \n", - "469 0.71 0 \n", - "472 0.20 0 \n", - "466 0.33 3 \n", - "462 1.00 0 \n", - "463 1.00 0 \n", - "464 NaN 0 \n", - "465 0.00 0 \n", - "467 NaN 0 \n", - "470 0.00 8 \n", - "471 0.00 1 " + " proportion significant num novel \n", + "1436 1.00 0 \n", + "1437 0.71 0 \n", + "1440 0.20 0 \n", + "1431 1.00 0 \n", + "1430 0.67 1 \n", + "... ... ... \n", + "1433 NaN 0 \n", + "1434 NaN 0 \n", + "1435 0.00 1 \n", + "1438 0.00 7 \n", + "1439 0.00 1 \n", + "\n", + "[11 rows x 16 columns]" ] }, - "execution_count": 52, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -4693,7 +4896,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 35, "id": "149cb60b", "metadata": {}, "outputs": [ @@ -4723,14 +4926,13 @@ " redundant\n", " standard\n", " standard no ontology\n", - " None\n", - " dav ontological synopsis\n", - " turbo no synopsis\n", " turbo ontological synopsis\n", - " rank based\n", - " dav no synopsis\n", + " turbo no synopsis\n", " turbo narrative synopsis\n", + " dav no synopsis\n", + " dav ontological synopsis\n", " dav narrative synopsis\n", + " rank based\n", " \n", " \n", " \n", @@ -4741,8 +4943,7 @@ " False\n", " 0.0\n", " 10.0\n", - " 218.0\n", - " 2.0\n", + " 4.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4753,11 +4954,10 @@ " \n", " 1\n", " GO:0072663\n", - " establishment of protein localization to peroxisome\n", + " establishment of protein localization to perox...\n", " True\n", " 1.0\n", " NaN\n", - " 236.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4773,7 +4973,6 @@ " True\n", " 2.0\n", " NaN\n", - " 235.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4789,7 +4988,6 @@ " False\n", " 3.0\n", " NaN\n", - " 277.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4805,7 +5003,6 @@ " True\n", " 4.0\n", " NaN\n", - " 226.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4815,61 +5012,26 @@ " NaN\n", " \n", " \n", - " 5\n", - " GO:0007031\n", - " peroxisome organization\n", - " True\n", - " 5.0\n", - " 2.0\n", - " 222.0\n", - " NaN\n", - " 0.0\n", - " 0.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 6\n", - " GO:0016558\n", - " protein import into peroxisome matrix\n", - " True\n", - " 6.0\n", - " 0.0\n", - " 278.0\n", - " 1.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 7\n", - " GO:0005778\n", - " peroxisomal membrane\n", + " 92\n", + " GO:0046872\n", + " metal ion binding\n", " False\n", - " 7.0\n", - " 1.0\n", - " 190.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 8\n", - " GO:0031903\n", - " microbody membrane\n", - " True\n", - " 8.0\n", " NaN\n", - " 193.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4877,31 +5039,14 @@ " NaN\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 9\n", - " GO:0016562\n", - " protein import into peroxisome matrix, receptor recycling\n", - " True\n", - " 9.0\n", " 3.0\n", - " 284.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", " \n", " \n", - " 10\n", - " GO:0044743\n", - " protein transmembrane import into intracellular organelle\n", - " True\n", - " 10.0\n", + " 93\n", + " GO:0016020\n", + " membrane\n", + " False\n", " NaN\n", - " 279.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4909,31 +5054,14 @@ " NaN\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 11\n", - " GO:0005777\n", - " peroxisome\n", - " True\n", - " 11.0\n", " 4.0\n", - " 188.0\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", " \n", " \n", - " 12\n", - " GO:0042579\n", - " microbody\n", - " True\n", - " 12.0\n", + " 94\n", + " GO:0070062\n", + " extracellular exosome\n", + " False\n", " NaN\n", - " 189.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4941,15 +5069,14 @@ " NaN\n", " NaN\n", " NaN\n", + " 5.0\n", " \n", " \n", - " 13\n", - " GO:0065002\n", - " intracellular protein transmembrane transport\n", - " True\n", - " 13.0\n", + " 95\n", + " GO:0005829\n", + " cytosol\n", + " False\n", " NaN\n", - " 280.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4957,15 +5084,14 @@ " NaN\n", " NaN\n", " NaN\n", + " 6.0\n", " \n", " \n", - " 14\n", - " GO:0071806\n", - " protein transmembrane transport\n", - " True\n", - " 14.0\n", + " 96\n", + " GO:0005737\n", + " cytoplasm\n", + " False\n", " NaN\n", - " 260.0\n", " NaN\n", " NaN\n", " NaN\n", @@ -4973,287 +5099,653 @@ " NaN\n", " NaN\n", " NaN\n", + " 7.0\n", + " \n", + " \n", + "\n", + "

97 rows × 12 columns

\n", + "" + ], + "text/plain": [ + " id label redundant \\\n", + "0 GO:0006625 protein targeting to peroxisome False \n", + "1 GO:0072663 establishment of protein localization to perox... True \n", + "2 GO:0072662 protein localization to peroxisome True \n", + "3 GO:0015919 peroxisomal membrane transport False \n", + "4 GO:0043574 peroxisomal transport True \n", + ".. ... ... ... \n", + "92 GO:0046872 metal ion binding False \n", + "93 GO:0016020 membrane False \n", + "94 GO:0070062 extracellular exosome False \n", + "95 GO:0005829 cytosol False \n", + "96 GO:0005737 cytoplasm False \n", + "\n", + " standard standard no ontology turbo ontological synopsis \\\n", + "0 0.0 10.0 4.0 \n", + "1 1.0 NaN NaN \n", + "2 2.0 NaN NaN \n", + "3 3.0 NaN NaN \n", + "4 4.0 NaN NaN \n", + ".. ... ... ... \n", + "92 NaN NaN NaN \n", + "93 NaN NaN NaN \n", + "94 NaN NaN NaN \n", + "95 NaN NaN NaN \n", + "96 NaN NaN NaN \n", + "\n", + " turbo no synopsis turbo narrative synopsis dav no synopsis \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + ".. ... ... ... \n", + "92 NaN NaN NaN \n", + "93 NaN NaN NaN \n", + "94 NaN NaN NaN \n", + "95 NaN NaN NaN \n", + "96 NaN NaN NaN \n", + "\n", + " dav ontological synopsis dav narrative synopsis rank based \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + ".. ... ... ... \n", + "92 NaN NaN 3.0 \n", + "93 NaN NaN 4.0 \n", + "94 NaN NaN 5.0 \n", + "95 NaN NaN 6.0 \n", + "96 NaN NaN 7.0 \n", + "\n", + "[97 rows x 12 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# terms_summary(peroxisome).style.highlight_min(axis=1, props='font-weight:bold', numeric_only=True)\n", + "terms_summary(peroxisome)" + ] + }, + { + "cell_type": "markdown", + "id": "ccb68aa7", + "metadata": {}, + "source": [ + "## Sensory Ataxia" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "8562caa4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelmethodhas top hitin top 5in top 10size overlapsimilaritynum termsnum GO termsnr size overlapnr similaritymean p valuemin p valuemax p valueproportion significantnum novel
15GO:0006605protein targeting1502N/AstandardTrue15.0NaN217.0NaNNaNNaNNaNNaNNaNNaNTrueTrue91.00e+009931.001.25e-021.95e-054.26e-021.000
16GO:0001881receptor recyclingTrue16.0NaN283.0NaNNaNNaNNaNNaNNaNNaN1503N/Astandard_no_ontologyFalseFalseFalse33.00e-014410.202.63e-013.13e-041.00e+000.750
17GO:0072594establishment of protein localization to organelle1497gpt-3.5-turboontological_synopsisTrue17.0NaN234.0NaNNaNNaNNaNNaNNaNNaNTrueTrue22.22e-013220.671.66e-041.95e-053.13e-041.000
18GO:0043112receptor metabolic process1500text-davinci-003ontological_synopsisTrue18.0NaN285.0NaNNaNNaNNaNNaNNaNNaN
19GO:0006886intracellular protein transportTrue19.0NaN220.0NaNNaNNaNNaNNaNNaNNaN
20GO:0033365protein localization to organelleTrue20.0NaN225.0NaNNaNNaNNaNNaNNaNNaN
21GO:0015031protein transportTrue21.0NaN223.0NaNNaNNaNNaNNaNNaNNaN
22GO:0045184establishment of protein localizationTrue22.0NaN227.0NaNNaNNaNNaNNaNNaNNaNFalseFalse21.00e-01171320.188.48e-011.95e-051.00e+000.150
23GO:0046907intracellular transport1506N/AclosureTrue23.0NaN228.0NaNNaNNaNNaNNaNNaNNaN
24GO:0140318protein transporter activityFalse24.011.0265.0NaNNaNNaNNaNNaNNaNNaNFalse98.51e-031360131400.009.89e-011.95e-051.00e+000.010
25GO:0071705nitrogen compound transportTrue25.0NaN232.0NaNNaNNaNNaNNaNNaNNaN...................................................
26GO:0006635fatty acid beta-oxidation1498gpt-3.5-turbonarrative_synopsisFalse26.05.0237.0NaNNaNNaNNaNNaNNaNNaN
27GO:0051649establishment of localization in cellTrue27.0NaN230.0NaNNaNNaNNaNNaNNaNNaNFalseFalse00.00e+004100.001.00e+001.00e+001.00e+000.000
28GO:0044721protein import into peroxisome matrix, substrate releaseTrue28.07.0372.0NaNNaNNaNNaNNaNNaNNaN1499text-davinci-003no_synopsisFalseFalseFalse00.00e+0017900.001.00e+001.00e+001.00e+000.004
29GO:0000268peroxisome targeting sequence binding1501text-davinci-003narrative_synopsisFalse29.0NaN84.0NaNNaNNaNNaNNaNNaNNaNFalseFalse00.00e+006200.001.00e+001.00e+001.00e+000.001
30GO:0043335protein unfolding1504N/ArandomFalse30.06.0367.0NaNNaNNaNNaNNaNNaNNaNFalseFalse00.00e+00141400.001.00e+001.00e+001.00e+000.007
31GO:0008104protein localizationTrue31.0NaN212.0NaNNaNNaN1505N/Arank_basedFalseFalseFalse00.00e+00151500.001.00e+001.00e+001.00e+000.000
\n", + "

11 rows × 16 columns

\n", + "
" + ], + "text/plain": [ + " model method has top hit in top 5 \\\n", + "1502 N/A standard True True \n", + "1503 N/A standard_no_ontology False False \n", + "1497 gpt-3.5-turbo ontological_synopsis True True \n", + "1500 text-davinci-003 ontological_synopsis True False \n", + "1506 N/A closure True False \n", + "... ... ... ... ... \n", + "1498 gpt-3.5-turbo narrative_synopsis False False \n", + "1499 text-davinci-003 no_synopsis False False \n", + "1501 text-davinci-003 narrative_synopsis False False \n", + "1504 N/A random False False \n", + "1505 N/A rank_based False False \n", + "\n", + " in top 10 size overlap similarity num terms num GO terms \\\n", + "1502 True 9 1.00e+00 9 9 \n", + "1503 False 3 3.00e-01 4 4 \n", + "1497 True 2 2.22e-01 3 2 \n", + "1500 False 2 1.00e-01 17 13 \n", + "1506 False 9 8.51e-03 1360 1314 \n", + "... ... ... ... ... ... \n", + "1498 False 0 0.00e+00 4 1 \n", + "1499 False 0 0.00e+00 17 9 \n", + "1501 False 0 0.00e+00 6 2 \n", + "1504 False 0 0.00e+00 14 14 \n", + "1505 False 0 0.00e+00 15 15 \n", + "\n", + " nr size overlap nr similarity mean p value min p value max p value \\\n", + "1502 3 1.00 1.25e-02 1.95e-05 4.26e-02 \n", + "1503 1 0.20 2.63e-01 3.13e-04 1.00e+00 \n", + "1497 2 0.67 1.66e-04 1.95e-05 3.13e-04 \n", + "1500 2 0.18 8.48e-01 1.95e-05 1.00e+00 \n", + "1506 0 0.00 9.89e-01 1.95e-05 1.00e+00 \n", + "... ... ... ... ... ... \n", + "1498 0 0.00 1.00e+00 1.00e+00 1.00e+00 \n", + "1499 0 0.00 1.00e+00 1.00e+00 1.00e+00 \n", + "1501 0 0.00 1.00e+00 1.00e+00 1.00e+00 \n", + "1504 0 0.00 1.00e+00 1.00e+00 1.00e+00 \n", + "1505 0 0.00 1.00e+00 1.00e+00 1.00e+00 \n", + "\n", + " proportion significant num novel \n", + "1502 1.00 0 \n", + "1503 0.75 0 \n", + "1497 1.00 0 \n", + "1500 0.15 0 \n", + "1506 0.01 0 \n", + "... ... ... \n", + "1498 0.00 0 \n", + "1499 0.00 4 \n", + "1501 0.00 1 \n", + "1504 0.00 7 \n", + "1505 0.00 0 \n", + "\n", + "[11 rows x 16 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ataxia = df.query(f\"{GENESET} == 'sensory ataxia-0'\").sort_values(\"similarity\", ascending=False)\n", + "ataxia[[MODEL, METHOD] + eval_summary_cols] " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "a744a21e", + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_colwidth', None)\n", + "pd.set_option('display.max_rows', None)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "884f460d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
modelmethodgo term idsnovel labels
1502N/Astandard[GO:0042552, GO:0008366, GO:0007272, GO:0007422, GO:0014037, GO:0010001, GO:0032287, GO:0006264, GO:0042063][]
1503N/Astandard_no_ontology[GO:0021680, GO:0032287, GO:0006264, GO:0007422][]
1497gpt-3.5-turboontological_synopsis[GO:0042552, GO:0007422][]
1500text-davinci-003ontological_synopsis[GO:0007155, GO:0016567, GO:0050794, GO:0042981, GO:0006264, GO:0006839, GO:0015886, GO:0090304, GO:0034214, GO:0010595, GO:0002639, GO:0098743, GO:0042552][]
1506N/Aclosure[GO:0140018, GO:0008045, GO:0009612, GO:0006259, GO:0030534, GO:0075732, GO:0002639, GO:0006268, GO:0046872, GO:0003700, GO:0030218, GO:0043161, GO:0048168, GO:0032287, GO:0043139, GO:0008285, GO:0004860, GO:0051402, GO:0000978, GO:0031643, GO:0060170, GO:0018158, GO:1904390, GO:1901184, GO:0007028, GO:0003682, GO:0005261, GO:0071260, GO:0051603, GO:0014037, GO:0004347, GO:0021666, GO:0048029, GO:0006418, GO:0008033, GO:0004561, GO:0007599, GO:0061665, GO:0006915, GO:0061630, GO:0043005, GO:0008408, GO:0005654, GO:0007622, GO:0046686, GO:0006812, GO:0036494, GO:0030154, GO:0002196, GO:0003674, GO:0043657, GO:0021675, GO:0010595, GO:0008366, GO:0042982, GO:1903912, GO:0034285, GO:0006261, GO:0031410, GO:0006986, GO:0003220, GO:0035578, GO:0016485, GO:0010977, GO:0016887, GO:0034142, GO:0071310, GO:1990837, GO:0060323, GO:0005759, GO:0071897, GO:0016567, GO:1904389, GO:0071407, GO:0043524, GO:0060173, GO:0008270, GO:0005789, GO:0048704, GO:0032060, GO:0005829, GO:0055085, GO:0015232, GO:0019901, GO:0005975, GO:0042564, GO:0035640, GO:0007611, GO:0021665, GO:0007165, GO:0032288, GO:0035264, GO:0042474, GO:0005643, GO:0006801, GO:0043231, GO:0043154, GO:0042645, GO:0035284, GO:0001701, ...][]
1496gpt-3.5-turbono_synopsis[GO:0031175, GO:0043209, GO:0005759][]
1498gpt-3.5-turbonarrative_synopsis[GO:0043209][]
1499text-davinci-003no_synopsis[GO:0007399, GO:0006119, GO:0006626, GO:0051604, GO:0045056, GO:0016192, GO:0000902, GO:0007163, GO:0030182][oxidative phosphorylation, protein targeting to mitochondrion, transcytosis, establishment or maintenance of cell polarity]
1501text-davinci-003narrative_synopsis[GO:0009117, GO:0007165][nucleotide metabolic process]
1504N/Arandom[GO:0021854, GO:0005654, GO:0007049, GO:0005737, GO:0005634, GO:0045087, GO:0071333, GO:0000139, GO:0033612, GO:0031982, GO:0019941, GO:0017018, GO:0000184, GO:0005743][hypothalamus development, cell cycle, innate immune response, Golgi membrane, receptor serine/threonine kinase binding, myosin phosphatase activity, nuclear-transcribed mRNA catabolic process, nonsense-mediated decay]
1505N/Arank_based[GO:0070062, GO:0042802, GO:0005654, GO:0005576, GO:0005737, GO:0005886, GO:0005634, GO:0005739, GO:0005829, GO:0016020, GO:0005524, GO:0005615, GO:0003723, GO:0006357, GO:0046872][]
\n", + "
" + ], + "text/plain": [ + " model method \\\n", + "1502 N/A standard \n", + "1503 N/A standard_no_ontology \n", + "1497 gpt-3.5-turbo ontological_synopsis \n", + "1500 text-davinci-003 ontological_synopsis \n", + "1506 N/A closure \n", + "1496 gpt-3.5-turbo no_synopsis \n", + "1498 gpt-3.5-turbo narrative_synopsis \n", + "1499 text-davinci-003 no_synopsis \n", + "1501 text-davinci-003 narrative_synopsis \n", + "1504 N/A random \n", + "1505 N/A rank_based \n", + "\n", + " go term ids \\\n", + "1502 [GO:0042552, GO:0008366, GO:0007272, GO:0007422, GO:0014037, GO:0010001, GO:0032287, GO:0006264, GO:0042063] \n", + "1503 [GO:0021680, GO:0032287, GO:0006264, GO:0007422] \n", + "1497 [GO:0042552, GO:0007422] \n", + "1500 [GO:0007155, GO:0016567, GO:0050794, GO:0042981, GO:0006264, GO:0006839, GO:0015886, GO:0090304, GO:0034214, GO:0010595, GO:0002639, GO:0098743, GO:0042552] \n", + "1506 [GO:0140018, GO:0008045, GO:0009612, GO:0006259, GO:0030534, GO:0075732, GO:0002639, GO:0006268, GO:0046872, GO:0003700, GO:0030218, GO:0043161, GO:0048168, GO:0032287, GO:0043139, GO:0008285, GO:0004860, GO:0051402, GO:0000978, GO:0031643, GO:0060170, GO:0018158, GO:1904390, GO:1901184, GO:0007028, GO:0003682, GO:0005261, GO:0071260, GO:0051603, GO:0014037, GO:0004347, GO:0021666, GO:0048029, GO:0006418, GO:0008033, GO:0004561, GO:0007599, GO:0061665, GO:0006915, GO:0061630, GO:0043005, GO:0008408, GO:0005654, GO:0007622, GO:0046686, GO:0006812, GO:0036494, GO:0030154, GO:0002196, GO:0003674, GO:0043657, GO:0021675, GO:0010595, GO:0008366, GO:0042982, GO:1903912, GO:0034285, GO:0006261, GO:0031410, GO:0006986, GO:0003220, GO:0035578, GO:0016485, GO:0010977, GO:0016887, GO:0034142, GO:0071310, GO:1990837, GO:0060323, GO:0005759, GO:0071897, GO:0016567, GO:1904389, GO:0071407, GO:0043524, GO:0060173, GO:0008270, GO:0005789, GO:0048704, GO:0032060, GO:0005829, GO:0055085, GO:0015232, GO:0019901, GO:0005975, GO:0042564, GO:0035640, GO:0007611, GO:0021665, GO:0007165, GO:0032288, GO:0035264, GO:0042474, GO:0005643, GO:0006801, GO:0043231, GO:0043154, GO:0042645, GO:0035284, GO:0001701, ...] \n", + "1496 [GO:0031175, GO:0043209, GO:0005759] \n", + "1498 [GO:0043209] \n", + "1499 [GO:0007399, GO:0006119, GO:0006626, GO:0051604, GO:0045056, GO:0016192, GO:0000902, GO:0007163, GO:0030182] \n", + "1501 [GO:0009117, GO:0007165] \n", + "1504 [GO:0021854, GO:0005654, GO:0007049, GO:0005737, GO:0005634, GO:0045087, GO:0071333, GO:0000139, GO:0033612, GO:0031982, GO:0019941, GO:0017018, GO:0000184, GO:0005743] \n", + "1505 [GO:0070062, GO:0042802, GO:0005654, GO:0005576, GO:0005737, GO:0005886, GO:0005634, GO:0005739, GO:0005829, GO:0016020, GO:0005524, GO:0005615, GO:0003723, GO:0006357, GO:0046872] \n", + "\n", + " novel labels \n", + "1502 [] \n", + "1503 [] \n", + "1497 [] \n", + "1500 [] \n", + "1506 [] \n", + "1496 [] \n", + "1498 [] \n", + "1499 [oxidative phosphorylation, protein targeting to mitochondrion, transcytosis, establishment or maintenance of cell polarity] \n", + "1501 [nucleotide metabolic process] \n", + "1504 [hypothalamus development, cell cycle, innate immune response, Golgi membrane, receptor serine/threonine kinase binding, myosin phosphatase activity, nuclear-transcribed mRNA catabolic process, nonsense-mediated decay] \n", + "1505 [] " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ataxia[[MODEL, METHOD, GO_TERM_IDS, NOVEL_LABELS]]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "06560bd8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5263,13 +5755,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5279,15 +5770,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5295,13 +5785,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5311,13 +5800,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5327,15 +5815,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5343,15 +5830,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5359,13 +5845,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -5375,15 +5860,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5391,13 +5875,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5407,14 +5890,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5423,14 +5905,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5439,14 +5920,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5455,15 +5935,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5471,14 +5950,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5487,14 +5965,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5503,14 +5980,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5519,14 +5995,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5535,14 +6010,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5551,14 +6025,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5567,15 +6040,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5583,14 +6055,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5599,14 +6070,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5615,14 +6085,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5631,14 +6100,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -5647,893 +6115,1247 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + "
idlabelredundantstandardturbo ontological synopsisdav ontological synopsisstandard no ontologyturbo no synopsisturbo narrative synopsisdav no synopsisdav narrative synopsisrank based
0GO:0042552myelinationFalse0.00.016.0NaNNaNNaNNaNNaNNaN
32GO:0070727cellular macromolecule localization1GO:0008366axon ensheathmentTrue32.01.0NaN216.0NaNNaNNaNNaN
33GO:0045046protein import into peroxisome membrane2GO:0007272ensheathment of neuronsTrue33.09.0375.02.0NaNNaNNaNNaNNaN
34GO:0000425pexophagy3GO:0007422peripheral nervous system developmentFalse34.08.090.0NaN3.01.0NaN3.0NaNNaNNaNNaN
35GO:0019395fatty acid oxidation4GO:0014037Schwann cell differentiationTrue35.04.0NaN241.0NaNNaNNaNNaN
36GO:0034440lipid oxidation5GO:0010001glial cell differentiationTrue36.05.0NaN243.0NaNNaNNaNNaN
37GO:0098588bounding membrane of organelle6GO:0032287peripheral nervous system myelin maintenanceTrue37.0NaN194.06.0NaNNaN1.0NaNNaNNaNNaN
38GO:0009062fatty acid catabolic processTrue38.0NaN238.0NaN7GO:0006264mitochondrial DNA replicationFalse7.0NaN6.02.0NaNNaNNaNNaN
39GO:0071702organic substance transport8GO:0042063gliogenesisTrue39.08.0NaN231.0NaNNaNNaNNaN
40GO:0033036macromolecule localizationTrue40.09GO:0021680cerebellar Purkinje cell layer developmentFalseNaN213.0NaNNaN0.0NaNNaNNaNNaN
41GO:0030242autophagy of peroxisomeTrue41.010nerve impulse transmissionNoneFalseNaN94.02.0NaNNaNNaNNaN
42GO:0072329monocarboxylic acid catabolic processTrue42.011GO:0007155cell adhesionFalseNaN247.0NaN0.0NaNNaNNaNNaN
43GO:0051641cellular localizationTrue43.012GO:0016567protein ubiquitinationFalseNaN215.0NaN1.0NaNNaNNaNNaN
44GO:0055085transmembrane transportTrue44.013neuronal developmentNoneFalseNaN259.0NaN2.0NaNNaNNaNNaN
45GO:0140036ubiquitin-dependent protein binding14GO:0050794regulation of cellular processFalse45.012.0411.0NaNNaN3.0NaNNaNNaNNaNNaN
46GO:0006996organelle organizationTrue46.015GO:0042981regulation of apoptotic processFalseNaN221.0NaN4.0NaNNaNNaNNaN
47GO:0140035ubiquitination-like modification-dependent protein bindingTrue47.016regulation of endoplasmic reticulum stressNoneFalseNaN410.0NaN5.0NaNNaNNaNNaN
48GO:0030258lipid modificationTrue48.017GO:0006839mitochondrial transportFalseNaN242.0NaN7.0NaNNaNNaNNaN
49GO:0044242cellular lipid catabolic processTrue49.018GO:0015886heme transportFalseNaN244.0NaN8.0NaNNaNNaNNaN
50GO:0046395carboxylic acid catabolic processTrue50.019GO:0090304nucleic acid metabolic processFalseNaN246.0NaN9.0NaNNaNNaNNaN
51GO:0016054organic acid catabolic processTrue51.020GO:0034214protein hexamerizationFalseNaN240.0NaN10.0NaNNaNNaNNaN
52GO:0000038very long-chain fatty acid metabolic process21glucose-6-phosphate metabolic processNoneFalse52.013.069.0NaNNaN11.0NaNNaNNaNNaNNaN
53GO:0031090organelle membraneTrue53.022GO:0010595positive regulation of endothelial cell migrationFalseNaN192.0NaN12.0NaNNaNNaNNaN
54GO:0006810transportTrue54.023GO:0002639positive regulation of immunoglobulin productionFalseNaN219.0NaN13.0NaNNaNNaNNaN
55GO:0016042lipid catabolic processTrue55.024positive regulation of transcriptionNoneFalseNaN239.0NaN14.0NaNNaNNaNNaN
56GO:0051234establishment of localizationTrue56.025GO:0098743cell aggregationFalseNaN229.0NaN15.0NaNNaNNaNNaN
57GO:0006631fatty acid metabolic processTrue57.026GO:0031175neuron projection developmentFalseNaNNaN72.08.0NaNNaN0.0NaNNaNNaNNaN
58GO:0005048signal sequence bindingTrue58.0NaN86.027GO:0043209myelin sheathFalseNaNNaNNaNNaN1.03.0NaNNaNNaN
59GO:0005782peroxisomal matrixTrue59.015.0195.028GO:0005759mitochondrial matrixFalseNaNNaNNaNNaN2.0NaNNaNNaNNaN
60GO:0031907microbody lumenTrue60.029mitochondrial respiratory chain complexNoneFalseNaN196.0NaNNaNNaN3.0NaNNaNNaNNaN
61GO:0044282small molecule catabolic processTrue61.030mitochondrial metabolismNoneFalseNaN245.0NaNNaNNaN4.0NaNNaNNaNNaN
62GO:0034614cellular response to reactive oxygen species31MONDO:0015626NoneFalseNaN14.0345.0NaNNaNNaNNaN0.0NaNNaNNaN
63GO:0001764neuron migration32MONDO:0007790NoneFalseNaN16.0146.0NaNNaNNaNNaN1.0NaNNaNNaN
64GO:0033328peroxisome membrane targeting sequence binding33MONDO:0005244NoneFalseNaN17.0339.0NaNNaNNaNNaN2.0NaNNaNNaN
65GO:0032994protein-lipid complex34membrane traffickingNoneFalseNaN18.0338.0NaNNaNNaNNaNNaN0.0NaNNaN
66GO:0005052peroxisome matrix targeting signal-1 binding35synthesis of proteinsNoneFalseNaN19.0160.0NaNNaNNaNNaNNaN1.0NaNNaN
67GO:0060152microtubule-based peroxisome localization36GO:0007399nervous system developmentFalseNaN20.0393.0NaNNaNNaNNaNNaN2.0NaNNaN
68GO:0005053peroxisome matrix targeting signal-2 binding37GO:0006119oxidative phosphorylationFalseNaNNaN161.0NaNNaNNaNNaNNaN3.0NaNNaN
69GO:0005829cytosol38GO:0006626protein targeting to mitochondrionFalseNaNNaN200.0NaNNaNNaN0.0NaN4.0NaNNaN
70GO:0032991protein-containing complex39GO:0051604protein maturationFalseNaNNaN289.0NaNNaNNaNNaNNaN5.0NaNNaN
71GO:0050680negative regulation of epithelial cell proliferation40protein processing in endoplasmic reticulumNoneFalseNaNNaN385.0NaNNaNNaNNaNNaN6.0NaNNaN
72GO:0031648protein destabilization41regulation of actin cytoskeletonNoneFalseNaNNaN336.07.0NaNNaNNaNNaN7.0NaNNaN
73GO:0019899enzyme binding42structural constituent of axonNoneFalseNaNNaN307.0NaNNaNNaNNaNNaN8.0NaNNaN
74GO:0008611ether lipid biosynthetic process43axonal transport of mitochondrial proteinNoneFalseNaNNaN268.0NaNNaNNaNNaNNaN9.0NaNNaN
75GO:0048468cell development44GO:0045056transcytosisFalseNaNNaN383.0NaNNaNNaNNaNNaN10.0NaNNaN
76GO:0070062extracellular exosome45translocation of proteinsNoneFalseNaNNaN407.0NaNNaNNaN4.0NaN11.0NaNNaN
77GO:0044183protein folding chaperone46GO:0016192vesicle-mediated transportFalseNaNNaN368.0NaNNaNNaNNaNNaN12.0NaNNaN
78GO:0040018positive regulation of multicellular organism growth47GO:0000902cell morphogenesisFalseNaNNaN355.0NaNNaNNaNNaNNaN13.0NaNNaN
79GO:0140597protein carrier chaperoneFalseNaNNaN413.0NaNNaNNaNNaNNaNNaNNaN
80GO:0030674protein-macromolecule adaptor activityFalseNaNNaN321.0NaNNaNNaNNaNNaNNaNNaN
81GO:0016561protein import into peroxisome matrix, translocationFalseNaNNaN282.0NaNNaNNaNNaNNaNNaNNaN
82GO:0005654nucleoplasmFalseNaNNaN185.0NaNNaNNaN2.0NaNNaNNaN
83GO:0005737cytoplasmFalseNaNNaN186.0NaNNaNNaN3.0NaNNaNNaN
84GO:0050821protein stabilizationFalseNaNNaN386.03.0NaNNaNNaNNaNNaNNaN
85GO:0008289lipid bindingFalseNaNNaN258.0NaNNaNNaNNaNNaNNaNNaN
86GO:0021795cerebral cortex cell migration48GO:0007163establishment or maintenance of cell polarityFalseNaNNaN315.0NaNNaNNaNNaNNaN14.0NaNNaN
87GO:0031333negative regulation of protein-containing complex assembly49nervous system morphogenesisNoneFalseNaNNaN325.0NaNNaNNaNNaNNaN15.0NaNNaN
88GO:0001958endochondral ossification50GO:0030182neuron differentiationFalseNaNNaN158.0NaNNaNNaNNaNNaN16.0NaNNaN
89GO:0016560protein import into peroxisome matrix, docking51neurodevelopmentNoneFalseNaNNaN281.0NaNNaNNaNNaNNaNNaN0.0NaN
90GO:0005524ATP binding52nerve damageNoneFalseNaNNaN163.0NaNNaNNaNNaNNaNNaN1.0NaN
91GO:0061630ubiquitin protein ligase activity53MESH:M0352612NoneFalseNaNNaN399.0NaNNaNNaNNaNNaNNaN2.0NaN
92GO:0016887ATP hydrolysis activity54GO:0009117nucleotide metabolic processFalseNaNNaN304.0NaNNaNNaNNaNNaNNaN3.0NaN
93GO:0007006mitochondrial membrane organization55mitochondrial functionNoneFalseNaNNaN250.0NaNNaNNaNNaNNaNNaN4.0NaN
94GO:0008270zinc ion binding56GO:0007165signal transductionFalseNaNNaN254.0NaNNaNNaNNaNNaNNaN5.0NaN
95GO:0097733photoreceptor cell cilium57GO:0070062extracellular exosomeFalseNaNNaN128.0NaNNaNNaNNaNNaNNaNNaN0.0
96GO:0007029endoplasmic reticulum organization58GO:0042802identical protein bindingFalseNaNNaN253.0NaNNaNNaNNaNNaNNaNNaN1.0
97GO:0005783endoplasmic reticulum59GO:0005654nucleoplasmFalseNaNNaN198.0NaNNaNNaNNaNNaNNaNNaN2.0
98GO:1990928response to amino acid starvation60GO:0005576extracellular regionFalseNaNNaN418.0NaNNaNNaNNaNNaNNaNNaN3.0
99GO:0042803protein homodimerization activity61GO:0005737cytoplasmFalseNaNNaN365.0NaNNaNNaNNaNNaNNaNNaN4.0
100GO:0016593Cdc73/Paf1 complex62GO:0005886plasma membraneFalseNaNNaN297.0NaNNaNNaNNaNNaNNaNNaN5.0
101GO:0048147negative regulation of fibroblast proliferation63GO:0005634nucleusFalseNaNNaN382.0NaNNaNNaNNaNNaNNaNNaN6.0
102GO:0044877protein-containing complex binding64GO:0005739mitochondrionFalseNaNNaN373.0NaNNaNNaNNaNNaNNaNNaN7.0
103GO:0008320protein transmembrane transporter activity65GO:0005829cytosolFalseNaNNaN262.0NaNNaNNaNNaNNaNNaNNaN8.0
104GO:0005739mitochondrion66GO:0016020membraneFalseNaNNaN187.0NaNNaNNaNNaNNaNNaNNaN9.0
105GO:0005794Golgi apparatus67GO:0005524ATP bindingFalseNaNNaN199.0NaNNaNNaNNaNNaNNaNNaN10.0
106GO:0001750photoreceptor outer segment68GO:0005615extracellular spaceFalseNaNNaN135.0NaNNaNNaNNaNNaNNaNNaN11.0
107GO:0016020membrane69GO:0003723RNA bindingFalseNaNNaN191.0NaNNaNNaN7.0NaNNaNNaN12.0
108GO:0006513protein monoubiquitination70GO:0006357regulation of transcription by RNA polymerase IIFalseNaNNaN202.0NaNNaNNaNNaNNaNNaNNaN13.0
109GO:0006457protein folding71GO:0046872metal ion bindingFalseNaNNaN201.0NaNNaNNaNNaNNaNNaNNaN14.0
110GO:0021895cerebral cortex neuron differentiationFalseNaNNaN318.0NaNNaNNaNNaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " id \\\n", + "0 GO:0042552 \n", + "1 GO:0008366 \n", + "2 GO:0007272 \n", + "3 GO:0007422 \n", + "4 GO:0014037 \n", + "5 GO:0010001 \n", + "6 GO:0032287 \n", + "7 GO:0006264 \n", + "8 GO:0042063 \n", + "9 GO:0021680 \n", + "10 nerve impulse transmission \n", + "11 GO:0007155 \n", + "12 GO:0016567 \n", + "13 neuronal development \n", + "14 GO:0050794 \n", + "15 GO:0042981 \n", + "16 regulation of endoplasmic reticulum stress \n", + "17 GO:0006839 \n", + "18 GO:0015886 \n", + "19 GO:0090304 \n", + "20 GO:0034214 \n", + "21 glucose-6-phosphate metabolic process \n", + "22 GO:0010595 \n", + "23 GO:0002639 \n", + "24 positive regulation of transcription \n", + "25 GO:0098743 \n", + "26 GO:0031175 \n", + "27 GO:0043209 \n", + "28 GO:0005759 \n", + "29 mitochondrial respiratory chain complex \n", + "30 mitochondrial metabolism \n", + "31 MONDO:0015626 \n", + "32 MONDO:0007790 \n", + "33 MONDO:0005244 \n", + "34 membrane trafficking \n", + "35 synthesis of proteins \n", + "36 GO:0007399 \n", + "37 GO:0006119 \n", + "38 GO:0006626 \n", + "39 GO:0051604 \n", + "40 protein processing in endoplasmic reticulum \n", + "41 regulation of actin cytoskeleton \n", + "42 structural constituent of axon \n", + "43 axonal transport of mitochondrial protein \n", + "44 GO:0045056 \n", + "45 translocation of proteins \n", + "46 GO:0016192 \n", + "47 GO:0000902 \n", + "48 GO:0007163 \n", + "49 nervous system morphogenesis \n", + "50 GO:0030182 \n", + "51 neurodevelopment \n", + "52 nerve damage \n", + "53 MESH:M0352612 \n", + "54 GO:0009117 \n", + "55 mitochondrial function \n", + "56 GO:0007165 \n", + "57 GO:0070062 \n", + "58 GO:0042802 \n", + "59 GO:0005654 \n", + "60 GO:0005576 \n", + "61 GO:0005737 \n", + "62 GO:0005886 \n", + "63 GO:0005634 \n", + "64 GO:0005739 \n", + "65 GO:0005829 \n", + "66 GO:0016020 \n", + "67 GO:0005524 \n", + "68 GO:0005615 \n", + "69 GO:0003723 \n", + "70 GO:0006357 \n", + "71 GO:0046872 \n", + "\n", + " label redundant standard \\\n", + "0 myelination False 0.0 \n", + "1 axon ensheathment True 1.0 \n", + "2 ensheathment of neurons True 2.0 \n", + "3 peripheral nervous system development False 3.0 \n", + "4 Schwann cell differentiation True 4.0 \n", + "5 glial cell differentiation True 5.0 \n", + "6 peripheral nervous system myelin maintenance True 6.0 \n", + "7 mitochondrial DNA replication False 7.0 \n", + "8 gliogenesis True 8.0 \n", + "9 cerebellar Purkinje cell layer development False NaN \n", + "10 None False NaN \n", + "11 cell adhesion False NaN \n", + "12 protein ubiquitination False NaN \n", + "13 None False NaN \n", + "14 regulation of cellular process False NaN \n", + "15 regulation of apoptotic process False NaN \n", + "16 None False NaN \n", + "17 mitochondrial transport False NaN \n", + "18 heme transport False NaN \n", + "19 nucleic acid metabolic process False NaN \n", + "20 protein hexamerization False NaN \n", + "21 None False NaN \n", + "22 positive regulation of endothelial cell migration False NaN \n", + "23 positive regulation of immunoglobulin production False NaN \n", + "24 None False NaN \n", + "25 cell aggregation False NaN \n", + "26 neuron projection development False NaN \n", + "27 myelin sheath False NaN \n", + "28 mitochondrial matrix False NaN \n", + "29 None False NaN \n", + "30 None False NaN \n", + "31 None False NaN \n", + "32 None False NaN \n", + "33 None False NaN \n", + "34 None False NaN \n", + "35 None False NaN \n", + "36 nervous system development False NaN \n", + "37 oxidative phosphorylation False NaN \n", + "38 protein targeting to mitochondrion False NaN \n", + "39 protein maturation False NaN \n", + "40 None False NaN \n", + "41 None False NaN \n", + "42 None False NaN \n", + "43 None False NaN \n", + "44 transcytosis False NaN \n", + "45 None False NaN \n", + "46 vesicle-mediated transport False NaN \n", + "47 cell morphogenesis False NaN \n", + "48 establishment or maintenance of cell polarity False NaN \n", + "49 None False NaN \n", + "50 neuron differentiation False NaN \n", + "51 None False NaN \n", + "52 None False NaN \n", + "53 None False NaN \n", + "54 nucleotide metabolic process False NaN \n", + "55 None False NaN \n", + "56 signal transduction False NaN \n", + "57 extracellular exosome False NaN \n", + "58 identical protein binding False NaN \n", + "59 nucleoplasm False NaN \n", + "60 extracellular region False NaN \n", + "61 cytoplasm False NaN \n", + "62 plasma membrane False NaN \n", + "63 nucleus False NaN \n", + "64 mitochondrion False NaN \n", + "65 cytosol False NaN \n", + "66 membrane False NaN \n", + "67 ATP binding False NaN \n", + "68 extracellular space False NaN \n", + "69 RNA binding False NaN \n", + "70 regulation of transcription by RNA polymerase II False NaN \n", + "71 metal ion binding False NaN \n", + "\n", + " turbo ontological synopsis dav ontological synopsis \\\n", + "0 0.0 16.0 \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 1.0 NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN 6.0 \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 2.0 NaN \n", + "11 NaN 0.0 \n", + "12 NaN 1.0 \n", + "13 NaN 2.0 \n", + "14 NaN 3.0 \n", + "15 NaN 4.0 \n", + "16 NaN 5.0 \n", + "17 NaN 7.0 \n", + "18 NaN 8.0 \n", + "19 NaN 9.0 \n", + "20 NaN 10.0 \n", + "21 NaN 11.0 \n", + "22 NaN 12.0 \n", + "23 NaN 13.0 \n", + "24 NaN 14.0 \n", + "25 NaN 15.0 \n", + "26 NaN NaN \n", + "27 NaN NaN \n", + "28 NaN NaN \n", + "29 NaN NaN \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 NaN NaN \n", + "33 NaN NaN \n", + "34 NaN NaN \n", + "35 NaN NaN \n", + "36 NaN NaN \n", + "37 NaN NaN \n", + "38 NaN NaN \n", + "39 NaN NaN \n", + "40 NaN NaN \n", + "41 NaN NaN \n", + "42 NaN NaN \n", + "43 NaN NaN \n", + "44 NaN NaN \n", + "45 NaN NaN \n", + "46 NaN NaN \n", + "47 NaN NaN \n", + "48 NaN NaN \n", + "49 NaN NaN \n", + "50 NaN NaN \n", + "51 NaN NaN \n", + "52 NaN NaN \n", + "53 NaN NaN \n", + "54 NaN NaN \n", + "55 NaN NaN \n", + "56 NaN NaN \n", + "57 NaN NaN \n", + "58 NaN NaN \n", + "59 NaN NaN \n", + "60 NaN NaN \n", + "61 NaN NaN \n", + "62 NaN NaN \n", + "63 NaN NaN \n", + "64 NaN NaN \n", + "65 NaN NaN \n", + "66 NaN NaN \n", + "67 NaN NaN \n", + "68 NaN NaN \n", + "69 NaN NaN \n", + "70 NaN NaN \n", + "71 NaN NaN \n", + "\n", + " standard no ontology turbo no synopsis turbo narrative synopsis \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 3.0 NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 1.0 NaN NaN \n", + "7 2.0 NaN NaN \n", + "8 NaN NaN NaN \n", + "9 0.0 NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "17 NaN NaN NaN \n", + "18 NaN NaN NaN \n", + "19 NaN NaN NaN \n", + "20 NaN NaN NaN \n", + "21 NaN NaN NaN \n", + "22 NaN NaN NaN \n", + "23 NaN NaN NaN \n", + "24 NaN NaN NaN \n", + "25 NaN NaN NaN \n", + "26 NaN 0.0 NaN \n", + "27 NaN 1.0 3.0 \n", + "28 NaN 2.0 NaN \n", + "29 NaN 3.0 NaN \n", + "30 NaN 4.0 NaN \n", + "31 NaN NaN 0.0 \n", + "32 NaN NaN 1.0 \n", + "33 NaN NaN 2.0 \n", + "34 NaN NaN NaN \n", + "35 NaN NaN NaN \n", + "36 NaN NaN NaN \n", + "37 NaN NaN NaN \n", + "38 NaN NaN NaN \n", + "39 NaN NaN NaN \n", + "40 NaN NaN NaN \n", + "41 NaN NaN NaN \n", + "42 NaN NaN NaN \n", + "43 NaN NaN NaN \n", + "44 NaN NaN NaN \n", + "45 NaN NaN NaN \n", + "46 NaN NaN NaN \n", + "47 NaN NaN NaN \n", + "48 NaN NaN NaN \n", + "49 NaN NaN NaN \n", + "50 NaN NaN NaN \n", + "51 NaN NaN NaN \n", + "52 NaN NaN NaN \n", + "53 NaN NaN NaN \n", + "54 NaN NaN NaN \n", + "55 NaN NaN NaN \n", + "56 NaN NaN NaN \n", + "57 NaN NaN NaN \n", + "58 NaN NaN NaN \n", + "59 NaN NaN NaN \n", + "60 NaN NaN NaN \n", + "61 NaN NaN NaN \n", + "62 NaN NaN NaN \n", + "63 NaN NaN NaN \n", + "64 NaN NaN NaN \n", + "65 NaN NaN NaN \n", + "66 NaN NaN NaN \n", + "67 NaN NaN NaN \n", + "68 NaN NaN NaN \n", + "69 NaN NaN NaN \n", + "70 NaN NaN NaN \n", + "71 NaN NaN NaN \n", + "\n", + " dav no synopsis dav narrative synopsis rank based \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "17 NaN NaN NaN \n", + "18 NaN NaN NaN \n", + "19 NaN NaN NaN \n", + "20 NaN NaN NaN \n", + "21 NaN NaN NaN \n", + "22 NaN NaN NaN \n", + "23 NaN NaN NaN \n", + "24 NaN NaN NaN \n", + "25 NaN NaN NaN \n", + "26 NaN NaN NaN \n", + "27 NaN NaN NaN \n", + "28 NaN NaN NaN \n", + "29 NaN NaN NaN \n", + "30 NaN NaN NaN \n", + "31 NaN NaN NaN \n", + "32 NaN NaN NaN \n", + "33 NaN NaN NaN \n", + "34 0.0 NaN NaN \n", + "35 1.0 NaN NaN \n", + "36 2.0 NaN NaN \n", + "37 3.0 NaN NaN \n", + "38 4.0 NaN NaN \n", + "39 5.0 NaN NaN \n", + "40 6.0 NaN NaN \n", + "41 7.0 NaN NaN \n", + "42 8.0 NaN NaN \n", + "43 9.0 NaN NaN \n", + "44 10.0 NaN NaN \n", + "45 11.0 NaN NaN \n", + "46 12.0 NaN NaN \n", + "47 13.0 NaN NaN \n", + "48 14.0 NaN NaN \n", + "49 15.0 NaN NaN \n", + "50 16.0 NaN NaN \n", + "51 NaN 0.0 NaN \n", + "52 NaN 1.0 NaN \n", + "53 NaN 2.0 NaN \n", + "54 NaN 3.0 NaN \n", + "55 NaN 4.0 NaN \n", + "56 NaN 5.0 NaN \n", + "57 NaN NaN 0.0 \n", + "58 NaN NaN 1.0 \n", + "59 NaN NaN 2.0 \n", + "60 NaN NaN 3.0 \n", + "61 NaN NaN 4.0 \n", + "62 NaN NaN 5.0 \n", + "63 NaN NaN 6.0 \n", + "64 NaN NaN 7.0 \n", + "65 NaN NaN 8.0 \n", + "66 NaN NaN 9.0 \n", + "67 NaN NaN 10.0 \n", + "68 NaN NaN 11.0 \n", + "69 NaN NaN 12.0 \n", + "70 NaN NaN 13.0 \n", + "71 NaN NaN 14.0 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "terms_summary(ataxia)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "76c0b8ee", + "metadata": {}, + "outputs": [], + "source": [ + "def retrieve_payload(geneset, method):\n", + " for comp in comps:\n", + " if comp.name == geneset:\n", + " return comp.payloads[method]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "17c4d4f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: Genes are mostly involved in nerve function and diseases, especially Charcot-Marie-Tooth disease.\n", + "\n", + "Mechanism: The genes are all involved in the process of myelination of nerve cells and other nervous system processes.\n", + "\n", + "Enriched Terms: Myelination; peripheral nervous system development; nerve impulse transmission\n" + ] + } + ], + "source": [ + "print(retrieve_payload(\"sensory ataxia-0\", \"gpt-3.5-turbo.ontological_synopsis\").response_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "2fb5f713", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: Genes are mostly involved in the maintenance and function of peripheral nerves in the human body.\n", + "Mechanism: Myelination and proper function of Schwann cells in peripheral nerves.\n", + "\n", + "Enriched Terms: Charcot-Marie-Tooth disease; Dejerine-Sottas syndrome; peripheral neuropathy; myelin sheath.\n", + "\n", + "Hypothesis: These genes are involved in the proper formation and maintenance of myelin sheaths in peripheral nerves, as mutations in these genes result in various forms of peripheral neuropathies, such as Charcot-Marie-Tooth disease and Dejerine-Sottas syndrome. Dysfunction in the myelin sheath formation or maintenance can lead to disrupted nerve conduction and neuropathy.\n" + ] + } + ], + "source": [ + "print(retrieve_payload(\"sensory ataxia-0\", \"gpt-3.5-turbo.narrative_synopsis\").response_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "652ef2a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: This set of genes is enriched for functions related to nervous system development and maintenance, as well as mitochondrial metabolism.\n", + "\n", + "Mechanism: These genes likely play roles in maintaining the proper functioning of neurons and supporting cells and in energy production in mitochondria.\n", + "\n", + "Enriched Terms: neuron projection development; myelin sheath; mitochondrial matrix; mitochondrial respiratory chain complex; mitochondrial metabolism.\n" + ] + } + ], + "source": [ + "print(retrieve_payload(\"sensory ataxia-0\", \"gpt-3.5-turbo.no_synopsis\").response_text)" + ] + }, + { + "cell_type": "markdown", + "id": "5876611c", + "metadata": {}, + "source": [ + "## Endocytosis" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "4df09ade", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6543,13 +7365,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6559,14 +7380,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6575,13 +7395,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6591,29 +7410,27 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6623,13 +7440,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6639,13 +7455,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6655,14 +7470,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6671,13 +7485,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6687,13 +7500,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6703,13 +7515,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6719,13 +7530,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6735,13 +7545,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6751,13 +7560,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6767,13 +7575,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6783,13 +7590,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6799,13 +7605,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6815,15 +7620,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6831,13 +7635,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6847,13 +7650,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6863,13 +7665,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6879,13 +7680,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6895,13 +7695,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6911,13 +7710,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6927,13 +7725,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6943,13 +7740,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6959,13 +7755,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6975,13 +7770,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -6991,13 +7785,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7007,13 +7800,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7023,13 +7815,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7039,13 +7830,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7055,13 +7845,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7071,14 +7860,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7087,13 +7875,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7103,13 +7890,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7119,14 +7905,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7135,13 +7920,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7151,13 +7935,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7167,13 +7950,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7183,13 +7965,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7199,13 +7980,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7215,14 +7995,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7231,14 +8010,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7247,13 +8025,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", @@ -7263,30 +8040,28 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7295,13 +8070,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7311,13 +8085,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7327,13 +8100,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7343,13 +8115,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7359,13 +8130,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7375,13 +8145,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7391,13 +8160,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7407,13 +8175,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7423,13 +8190,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7439,13 +8205,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7455,13 +8220,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7471,13 +8235,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7487,13 +8250,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7503,14 +8265,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7519,14 +8280,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7535,15 +8295,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7551,15 +8310,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7567,15 +8325,14 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -7583,9030 +8340,1382 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idlabelredundantstandardstandard no ontologyturbo ontological synopsisturbo no synopsisturbo narrative synopsisdav narrative synopsisdav no synopsisrank baseddav ontological synopsis
111GO:0050905neuromuscular process0GO:0006907pinocytosisFalse0.01.03.0NaNNaN389.0NaNNaNNaNNaN
1GO:0006897endocytosisTrue1.06.00.00.00.020.0NaNNaNNaN
112GO:0031267small GTPase bindingFalseNaNNaN323.02GO:0044351macropinocytosisTrue2.00.0NaNNaNNaNNaN
113BFO:0000003occurrentFalseNaN3GO:0016192vesicle-mediated transportTrue3.0NaN67.0NaNNaNNaNNaN
114BFO:0000015process4GO:0030100regulation of endocytosisFalse4.0NaNNaN68.0NaNNaNNaNNaNNaN
115GO:0006082organic acid metabolic processFalseNaN5GO:0006810transportTrue5.0NaN70.0NaNNaNNaNNaN
116GO:0006629lipid metabolic processFalse6GO:0051234establishment of localizationTrue6.0NaNNaN71.0NaNNaNNaNNaN1.0NaNNaN
117GO:0008150biological_processFalseNaN7GO:0045807positive regulation of endocytosisTrue7.0NaN73.0NaNNaNNaNNaN
118GO:0008152metabolic processFalseNaN8GO:0060627regulation of vesicle-mediated transportTrue8.0NaN74.0NaNNaNNaNNaN
119GO:0009987cellular processFalseNaN9GO:0051179localizationTrue9.0NaN75.0NaNNaNNaNNaN
120GO:0019752carboxylic acid metabolic process10GO:0031410cytoplasmic vesicleFalse10.0NaNNaN76.0NaNNaNNaNNaNNaN
121GO:0032787monocarboxylic acid metabolic processFalseNaN11GO:0097708intracellular vesicleTrue11.0NaN77.0NaNNaNNaNNaN
122GO:0043436oxoacid metabolic processFalseNaNNaN78.012GO:0050766positive regulation of phagocytosisTrue12.04.0NaNNaNNaNNaN
123GO:0044237cellular metabolic processFalseNaN13GO:0048518positive regulation of biological processTrue13.0NaN79.0NaNNaNNaNNaN
124GO:0044238primary metabolic processFalseNaN14GO:0050764regulation of phagocytosisTrue14.0NaN80.0NaNNaNNaNNaN
125GO:0044255cellular lipid metabolic processFalseNaN15GO:0051128regulation of cellular component organizationTrue15.0NaN81.0NaNNaNNaNNaN
126GO:0044281small molecule metabolic processFalseNaN16GO:0031982vesicleTrue16.0NaN82.0NaNNaNNaNNaN
127GO:0071704organic substance metabolic process17GO:0150094amyloid-beta clearance by cellular catabolic processFalseNaNNaN83.017.03.0NaNNaNNaNNaN
128GO:0003674molecular_functionFalseNaNNaN85.018GO:0006909phagocytosisTrue18.017.0NaNNaNNaNNaN
129GO:0005488bindingFalseNaN19GO:0051049regulation of transportTrue19.0NaN87.0NaNNaNNaNNaN
130GO:0033218amide bindingFalseNaNNaN88.0NaNNaN20GO:0006898receptor-mediated endocytosisTrue20.02.04.01.0NaNNaNNaNNaN
131GO:0042277peptide bindingFalseNaN21GO:0051050positive regulation of transportTrue21.0NaN89.0NaNNaNNaNNaN
132GO:0006914autophagyFalseNaNNaN91.022GO:0005041low-density lipoprotein particle receptor activityTrue22.05.0NaNNaNNaNNaN
133GO:0009056catabolic processFalseNaN23GO:0030139endocytic vesicleTrue23.0NaN92.0NaNNaNNaNNaN
134GO:0016236macroautophagyFalseNaN24GO:0030228lipoprotein particle receptor activityTrue24.0NaN93.0NaNNaNNaNNaN
135GO:0044248cellular catabolic processFalseNaNNaN95.025GO:0030666endocytic vesicle membraneTrue25.018.0NaNNaNNaNNaN
136GO:0061912selective autophagyFalseNaNNaN96.026GO:0097242amyloid-beta clearanceTrue26.022.0NaNNaNNaNNaN
137GO:0061919process utilizing autophagic mechanismFalseNaN27GO:0051130positive regulation of cellular component organizationTrue27.0NaN97.0NaNNaNNaNNaN
138BFO:0000002continuantFalseNaNNaN98.028GO:0060907positive regulation of macrophage cytokine productionTrue28.08.0NaNNaNNaNNaN
139BFO:0000004independent continuantFalseNaN29GO:0032879regulation of localizationTrue29.0NaN99.0NaNNaNNaNNaN
140BFO:0000040material entityFalseNaN30GO:0048522positive regulation of cellular processTrue30.0NaN100.0NaNNaNNaNNaN
141CARO:0000000anatomical entity31GO:0002277myeloid dendritic cell activation involved in immune responseFalseNaNNaN101.031.09.0NaNNaNNaNNaN
142CARO:0000003connected anatomical structureFalseNaN32GO:0030659cytoplasmic vesicle membraneTrue32.0NaN102.0NaNNaNNaNNaN
143CARO:0000006material anatomical entityFalseNaN33GO:0012506vesicle membraneTrue33.0NaN103.0NaNNaNNaNNaN
144CARO:0030000biological entityFalseNaN34GO:0061081positive regulation of myeloid leukocyte cytokine production involved in immune responseTrue34.0NaN104.0NaNNaNNaNNaN
145CL:0000000cellFalseNaN35GO:0010935regulation of macrophage cytokine productionTrue35.0NaN105.0NaNNaNNaNNaN
146CL:0000003native cell36GO:0048583regulation of response to stimulusFalse36.0NaNNaN106.0NaNNaNNaNNaNNaN
147CL:0000006neuronal receptor cellFalseNaNNaN107.037GO:1905167positive regulation of lysosomal protein catabolic processTrue37.010.0NaNNaNNaNNaN
148CL:0000101sensory neuronFalseNaN38GO:0009894regulation of catabolic processTrue38.0NaN108.0NaNNaNNaNNaN
149CL:0000197sensory receptor cell39GO:0023051regulation of signalingFalse39.0NaNNaN109.0NaNNaNNaNNaNNaN
150CL:0000210photoreceptor cellFalseNaN40GO:0051641cellular localizationTrue40.0NaN110.0NaNNaNNaNNaN
151CL:0000211electrically active cellFalseNaN41GO:1904352positive regulation of protein catabolic process in the vacuoleTrue41.0NaN111.0NaNNaNNaNNaN
152CL:0000255eukaryotic cellFalseNaNNaN112.042GO:0070508cholesterol importTrue42.013.0NaNNaNNaNNaN
153CL:0000393electrically responsive cellFalseNaN43GO:0031347regulation of defense responseTrue43.0NaN113.0NaNNaNNaNNaN
154CL:0000404electrically signaling cell44GO:0005794Golgi apparatusFalseNaNNaN114.044.014.0NaNNaNNaNNaN
155CL:0000526afferent neuron45GO:1901700response to oxygen-containing compoundFalse45.0NaNNaN115.0NaNNaNNaNNaNNaN
156CL:0000540neuron46GO:0061024membrane organizationFalse46.0NaNNaN116.0NaNNaNNaNNaNNaN
157CL:0000548animal cellFalseNaN47GO:0009966regulation of signal transductionTrue47.0NaN117.0NaNNaNNaNNaN
158CL:0002319neural cellFalseNaNNaN118.0NaN48GO:0015031protein transportTrue48.0NaNNaNNaN2.018.0NaNNaNNaN
159CL:0002371somatic cell49GO:0048523negative regulation of cellular processFalse49.0NaNNaN119.0NaNNaNNaNNaNNaN
160GO:0005575cellular_component50GO:0032429regulation of phospholipase A2 activityFalseNaNNaN120.07.0NaNNaNNaNNaN
161GO:0005929cilium51GO:0034381plasma lipoprotein particle clearanceFalseNaNNaN121.011.0NaNNaNNaNNaN
162GO:0042995cell projection52GO:0031623receptor internalizationFalseNaNNaN122.012.0NaNNaNNaNNaN
163GO:0043005neuron projection53GO:0009931calcium-dependent protein serine/threonine kinase activityFalseNaNNaN123.015.0NaNNaNNaNNaN
164GO:0043226organelle54GO:0005905clathrin-coated pitFalseNaNNaN124.016.0NaNNaNNaNNaN
165GO:0043227membrane-bounded organelle55GO:0032050clathrin heavy chain bindingFalseNaNNaN125.019.0NaNNaNNaNNaN
166GO:0097730non-motile cilium56GO:0030299intestinal cholesterol absorptionFalseNaNNaN126.020.0NaNNaNNaNNaN
167GO:00977319+0 non-motile cilium57GO:0001540amyloid-beta bindingFalseNaNNaN127.021.0NaNNaNNaNNaN
168GO:0110165cellular anatomical entity58GO:0034383low-density lipoprotein particle clearanceFalseNaNNaN129.023.0NaNNaNNaNNaN
169GO:0120025plasma membrane bounded cell projection59GO:0032760positive regulation of tumor necrosis factor productionFalseNaNNaN130.024.0NaNNaNNaNNaN
170PR:000050567protein-containing material entity60GO:0042953lipoprotein transportFalseNaNNaN131.025.0NaNNaNNaNNaN
171UBERON:0000061anatomical structure61GO:0071404cellular response to low-density lipoprotein particle stimulusFalseNaNNaN132.026.0NaNNaNNaNNaN
172UBERON:0000465material anatomical entity62GO:0030169low-density lipoprotein particle bindingFalseNaNNaN133.027.0NaNNaNNaNNaN
173UBERON:0001062anatomical entity63GO:0055085transmembrane transportFalseNaNNaN134.0NaN1.0NaNNaNNaNNaN
174GO:0007275multicellular organism development64GO:0043001Golgi to plasma membrane protein transportFalseNaNNaN136.0NaN2.0NaNNaNNaNNaN
175GO:0007399nervous system development65macromolecule transportNoneFalseNaNNaN137.0NaNNaN2.0NaNNaNNaNNaN
176GO:0022008neurogenesis66GO:0000165MAPK cascadeFalseNaNNaN138.0NaNNaN3.0NaNNaNNaNNaN
177GO:0030154cell differentiation67intracellular traffickingNoneFalseNaNNaN139.0NaNNaN4.0NaNNaNNaNNaN
178GO:0032501multicellular organismal process68GO:0003924GTPase activityFalseNaNNaN140.0NaNNaNNaN1.0NaNNaNNaNNaN
179GO:0032502developmental process69intracellular signalingNoneFalseNaNNaN141.0NaNNaNNaNNaN3.0NaN1.0NaNNaN
180GO:0048699generation of neurons70cytoskeletal reorganizationNoneFalseNaNNaN142.0NaNNaNNaN4.0NaNNaNNaNNaN
181GO:0048731system development71GO:0055088lipid homeostasisFalseNaNNaN143.0NaNNaNNaN5.0NaNNaNNaNNaN
182GO:0048856anatomical structure development72lysosomal degradationNoneFalseNaNNaN144.0NaNNaNNaNNaN6.019.0NaNNaNNaN
183GO:0048869cellular developmental process73the results of the term enrichment test on the list of given genes indicate that the genes are predominately involved in cellular processes such as intracellular signalingNoneFalseNaNNaN145.0NaNNaNNaNNaN0.0NaNNaNNaN
184GO:0016477cell migration74cellular organizationNoneFalseNaNNaN147.0NaNNaNNaNNaN22.0NaNNaNNaN
185GO:0048870cell motility75GO:0005515protein bindingFalseNaNNaN148.0NaNNaNNaNNaN21.0NaNNaNNaN
186GO:0001501skeletal system development76GO:0051260protein homooligomerizationFalseNaNNaN149.0NaNNaNNaNNaN4.0NaNNaNNaN
187GO:0009653anatomical structure morphogenesis77and macropinocytosis. the predominant underlying pathway is protein transportNoneFalseNaNNaN150.0NaNNaNNaNNaN6.0NaNNaNNaN
188GO:0009887animal organ morphogenesis78from the endoplasmic reticulum to the golgiNoneFalseNaNNaN151.0NaNNaNNaNNaN7.0NaNNaNNaN
189GO:0048513animal organ development79the nucleusNoneFalseNaNNaN152.0NaNNaNNaNNaNNaNNaNNaN
190GO:0048705skeletal system morphogenesisFalseNaNNaN153.0NaNNaNNaNNaN8.0NaNNaNNaN
191GO:0060348bone development80and then the plasma membrane. the enriched terms include ‘protein transport’NoneFalseNaNNaN154.0NaNNaNNaNNaN9.0NaNNaNNaN
192GO:0060349bone morphogenesis81'lysosomal degradation'NoneFalseNaNNaN155.0NaNNaNNaNNaN10.0NaNNaNNaN
193GO:0060350endochondral bone morphogenesis82'endocytosis'NoneFalseNaNNaN156.0NaNNaNNaNNaN11.0NaNNaNNaN
194GO:0001503ossification83'cellular organization'NoneFalseNaNNaN157.0NaNNaNNaNNaN12.0NaNNaNNaN
195GO:0036075replacement ossification84'protein binding'NoneFalseNaNNaN159.0NaNNaNNaNNaN13.0NaNNaNNaN
196GO:0000166nucleotide binding85‘cellular signaling’NoneFalseNaNNaN162.0NaNNaNNaNNaN14.0NaNNaNNaN
197GO:0017076purine nucleotide binding86‘gtpase activity’NoneFalseNaNNaN164.0NaNNaNNaNNaN15.0NaNNaNNaN
198GO:0030554adenyl nucleotide binding87‘macropinocytosis’NoneFalseNaNNaN165.0NaNNaNNaNNaN16.0NaNNaNNaN
199GO:0032553ribonucleotide binding88and ‘protein homooligomerization’. \\n\\nsummary: genes are predominantly involved in cellular processes such as intracellular signalingNoneFalseNaNNaN166.0NaNNaNNaNNaN17.0NaNNaNNaN
200GO:0032555purine ribonucleotide binding89and macropinocytosis. \\n\\nmechanism: protein transport from the endoplasmic reticulum to the golgiNoneFalseNaNNaN167.0NaNNaNNaNNaN23.0NaNNaNNaN
201GO:0032559adenyl ribonucleotide binding90GO:0005634nucleusFalseNaNNaN168.0NaNNaNNaNNaNNaN24.0NaN4.0NaN
202GO:0035639purine ribonucleoside triphosphate binding91then the plasma membrane. \\n\\nenrichedNoneFalseNaNNaN169.0NaNNaNNaNNaN25.0NaNNaNNaN
203GO:0036094small molecule binding92GO:0004672protein kinase activityFalseNaNNaN170.0NaNNaNNaNNaNNaN0.0NaNNaN
204GO:0043167ion binding93GO:0000902cell morphogenesisFalseNaNNaN171.0NaNNaNNaNNaNNaN2.0NaNNaN
205GO:0043168anion binding94GO:0048468cell developmentFalseNaNNaN172.0NaNNaNNaNNaNNaN3.0NaNNaN
206GO:0097159organic cyclic compound binding95GO:0007155cell adhesionFalseNaNNaN173.0NaNNaNNaNNaNNaN4.0NaNNaN
207GO:0097367carbohydrate derivative binding96GO:0010467gene expressionFalseNaNNaN174.0NaNNaNNaNNaNNaN5.0NaNNaN
208GO:1901265nucleoside phosphate binding97protein-protein interactionNoneFalseNaNNaN175.0NaNNaNNaNNaNNaN6.0NaNNaN
209GO:1901363heterocyclic compound binding98gtp-dependent protein binding activityNoneFalseNaNNaN176.0NaNNaNNaNNaNNaNNaNNaN0.0
210GO:0005622intracellular anatomical structure99GO:0004674protein serine/threonine kinase activityFalseNaNNaN177.0NaNNaNNaNNaNNaNNaNNaN
211GO:0005634nucleusFalseNaNNaN178.0NaNNaNNaN1.0NaNNaNNaN
212GO:0031974membrane-enclosed lumen100protein ubiquitin ligase activityNoneFalseNaNNaN179.0NaNNaNNaNNaNNaNNaNNaN2.0
213GO:0031981nuclear lumen101GO:0032880regulation of protein localizationFalseNaNNaN180.0NaNNaNNaNNaNNaNNaNNaN3.0
214GO:0043229intracellular organelle102signaling receptor binding activityNoneFalseNaNNaN181.0NaNNaNNaNNaNNaNNaNNaN4.0
215GO:0043231intracellular membrane-bounded organelle103t cell receptor binding activityNoneFalseNaNNaN182.0NaNNaNNaNNaNNaNNaNNaN5.0
216GO:0043233organelle lumen104GO:0005739mitochondrionFalseNaNNaN183.0NaNNaNNaNNaNNaNNaN0.0NaN
217GO:0070013intracellular organelle lumen105GO:0006357regulation of transcription by RNA polymerase IIFalseNaNNaN184.0NaNNaNNaNNaNNaNNaN1.0NaN
218GO:0012505endomembrane system106GO:0005615extracellular spaceFalseNaNNaN197.0NaNNaNNaNNaNNaNNaN2.0NaN
219GO:0006807nitrogen compound metabolic process107GO:0003723RNA bindingFalseNaNNaN203.0NaNNaNNaNNaNNaNNaN3.0NaN
220GO:0016567protein ubiquitination108GO:0070062extracellular exosomeFalseNaNNaN204.0NaNNaNNaNNaNNaNNaN5.0NaN
221GO:0019538protein metabolic process109GO:0005524ATP bindingFalseNaNNaN205.0NaNNaNNaNNaN3.0NaN6.0NaN
222GO:0032446protein modification by small protein conjugation110GO:0042802identical protein bindingFalseNaNNaN206.0NaNNaNNaNNaNNaNNaN7.0NaN
223GO:0036211protein modification process111GO:0005829cytosolFalseNaNNaN207.0NaNNaNNaNNaNNaNNaN8.0NaN
224GO:0043170macromolecule metabolic process112GO:0005654nucleoplasmFalseNaNNaN208.0NaNNaNNaNNaNNaNNaN9.0NaN
225GO:0043412macromolecule modification113GO:0005737cytoplasmFalseNaNNaN209.0NaNNaNNaNNaNNaNNaN10.0NaN
226GO:0070647protein modification by small protein conjugation or removal114GO:0016020membraneFalseNaNNaN210.0NaNNaNNaNNaNNaNNaN11.0NaN
227GO:1901564organonitrogen compound metabolic process115GO:0005886plasma membraneFalseNaNNaN211.0NaNNaNNaNNaNNaNNaN12.0NaN
228GO:0051179localization116GO:0005576extracellular regionFalseNaNNaN214.0NaNNaNNaNNaNNaNNaN13.0NaN
229GO:0016043cellular component organization117GO:0045944positive regulation of transcription by RNA polymerase IIFalseNaNNaN224.0NaNNaNNaNNaNNaNNaN14.0NaN
230GO:0071840cellular component organization or biogenesis118GO:0046872metal ion bindingFalseNaNNaN233.0NaNNaNNaNNaNNaNNaN15.0NaN
231GO:1901575organic substance catabolic processFalseNaNNaN248.0NaNNaNNaNNaNNaNNaNNaN
232GO:0007005mitochondrion organizationFalseNaNNaN249.0NaNNaNNaNNaNNaNNaNNaN
233GO:0061024membrane organizationFalseNaNNaN251.0NaNNaNNaNNaNNaNNaNNaN
234GO:0010256endomembrane system organizationFalseNaNNaN252.0NaNNaNNaNNaNNaNNaNNaN
235GO:0043169cation bindingFalseNaNNaN255.0NaNNaNNaNNaNNaNNaNNaN
236GO:0046872metal ion bindingFalseNaNNaN256.0NaNNaNNaN6.0NaNNaNNaN
237GO:0046914transition metal ion bindingFalseNaNNaN257.0NaNNaNNaNNaNNaNNaNNaN
238GO:0005215transporter activityFalseNaNNaN261.0NaNNaNNaNNaNNaNNaNNaN
239GO:0022857transmembrane transporter activityFalseNaNNaN263.0NaNNaNNaNNaNNaNNaNNaN
240GO:0022884macromolecule transmembrane transporter activityFalseNaNNaN264.0NaNNaNNaNNaNNaNNaNNaN
241GO:0006662glycerol ether metabolic processFalseNaNNaN266.0NaNNaNNaNNaNNaNNaNNaN
242GO:0008610lipid biosynthetic processFalseNaNNaN267.0NaNNaNNaNNaNNaNNaNNaN
243GO:0009058biosynthetic processFalseNaNNaN269.0NaNNaNNaNNaNNaNNaNNaN
244GO:0018904ether metabolic processFalseNaNNaN270.0NaNNaNNaNNaNNaNNaNNaN
245GO:0044249cellular biosynthetic processFalseNaNNaN271.0NaNNaNNaNNaNNaNNaNNaN
246GO:0046485ether lipid metabolic processFalseNaNNaN272.0NaNNaNNaNNaNNaNNaNNaN
247GO:0046504glycerol ether biosynthetic processFalseNaNNaN273.0NaNNaNNaNNaNNaNNaNNaN
248GO:0097384cellular lipid biosynthetic processFalseNaNNaN274.0NaNNaNNaNNaNNaNNaNNaN
249GO:1901503ether biosynthetic processFalseNaNNaN275.0NaNNaNNaNNaNNaNNaNNaN
250GO:1901576organic substance biosynthetic processFalseNaNNaN276.0NaNNaNNaNNaNNaNNaNNaN
251GO:0000428DNA-directed RNA polymerase complexFalseNaNNaN286.0NaNNaNNaNNaNNaNNaNNaN
252GO:0016591RNA polymerase II, holoenzymeFalseNaNNaN287.0NaNNaNNaNNaNNaNNaNNaN
253GO:0030880RNA polymerase complexFalseNaNNaN288.0NaNNaNNaNNaNNaNNaNNaN
254GO:0055029nuclear DNA-directed RNA polymerase complexFalseNaNNaN290.0NaNNaNNaNNaNNaNNaNNaN
255GO:0061695transferase complex, transferring phosphorus-containing groupsFalseNaNNaN291.0NaNNaNNaNNaNNaNNaNNaN
256GO:0140513nuclear protein-containing complexFalseNaNNaN292.0NaNNaNNaNNaNNaNNaNNaN
257GO:0140535intracellular protein-containing complexFalseNaNNaN293.0NaNNaNNaNNaNNaNNaNNaN
258GO:1902494catalytic complexFalseNaNNaN294.0NaNNaNNaNNaNNaNNaNNaN
259GO:1990234transferase complexFalseNaNNaN295.0NaNNaNNaNNaNNaNNaNNaN
260GO:0008023transcription elongation factor complexFalseNaNNaN296.0NaNNaNNaNNaNNaNNaNNaN
261GO:0140657ATP-dependent activityFalseNaNNaN298.0NaNNaNNaNNaNNaNNaNNaN
262GO:0003824catalytic activityFalseNaNNaN299.0NaNNaNNaNNaNNaNNaNNaN
263GO:0016462pyrophosphatase activityFalseNaNNaN300.0NaNNaNNaNNaNNaNNaNNaN
264GO:0016787hydrolase activityFalseNaNNaN301.0NaNNaNNaNNaNNaNNaNNaN
265GO:0016817hydrolase activity, acting on acid anhydridesFalseNaNNaN302.0NaNNaNNaNNaNNaNNaNNaN
266GO:0016818hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydridesFalseNaNNaN303.0NaNNaNNaNNaNNaNNaNNaN
267GO:0017111ribonucleoside triphosphate phosphatase activityFalseNaNNaN305.0NaNNaNNaNNaNNaNNaNNaN
268GO:0005515protein bindingFalseNaNNaN306.0NaNNaNNaNNaNNaNNaNNaN
269GO:0007417central nervous system developmentFalseNaNNaN308.0NaNNaNNaNNaNNaNNaNNaN
270GO:0007420brain developmentFalseNaNNaN309.0NaNNaNNaNNaNNaNNaNNaN
271GO:0021537telencephalon developmentFalseNaNNaN310.0NaNNaNNaNNaNNaNNaNNaN
272GO:0021543pallium developmentFalseNaNNaN311.0NaNNaNNaNNaNNaNNaNNaN
273GO:0021987cerebral cortex developmentFalseNaNNaN312.0NaNNaNNaNNaNNaNNaNNaN
274GO:0030900forebrain developmentFalseNaNNaN313.0NaNNaNNaNNaNNaNNaNNaN
275GO:0060322head developmentFalseNaNNaN314.0NaNNaNNaNNaNNaNNaNNaN
276GO:0021885forebrain cell migrationFalseNaNNaN316.0NaNNaNNaNNaNNaNNaNNaN
277GO:0022029telencephalon cell migrationFalseNaNNaN317.0NaNNaNNaNNaNNaNNaNNaN
278GO:0021953central nervous system neuron differentiationFalseNaNNaN319.0NaNNaNNaNNaNNaNNaNNaN
279GO:0030182neuron differentiationFalseNaNNaN320.0NaNNaNNaNNaNNaNNaNNaN
280GO:0060090molecular adaptor activityFalseNaNNaN322.0NaNNaNNaNNaNNaNNaNNaN
281GO:0051020GTPase bindingFalseNaNNaN324.0NaNNaNNaNNaNNaNNaNNaN
282GO:0043254regulation of protein-containing complex assemblyFalseNaNNaN326.0NaNNaNNaNNaNNaNNaNNaN
283GO:0044087regulation of cellular component biogenesisFalseNaNNaN327.0NaNNaNNaNNaNNaNNaNNaN
284GO:0048519negative regulation of biological processFalseNaNNaN328.0NaNNaNNaNNaNNaNNaNNaN
285GO:0048523negative regulation of cellular processFalseNaNNaN329.0NaNNaNNaNNaNNaNNaNNaN
286GO:0050789regulation of biological processFalseNaNNaN330.0NaNNaNNaNNaNNaNNaNNaN
287GO:0050794regulation of cellular processFalseNaNNaN331.0NaNNaNNaNNaNNaNNaNNaN
288GO:0051128regulation of cellular component organizationFalseNaNNaN332.0NaNNaNNaNNaNNaNNaNNaN
289GO:0051129negative regulation of cellular component organizationFalseNaNNaN333.0NaNNaNNaNNaNNaNNaNNaN
290GO:0065007biological regulationFalseNaNNaN334.0NaNNaNNaNNaNNaNNaNNaN
291GO:0031647regulation of protein stabilityFalseNaNNaN335.0NaNNaNNaNNaNNaNNaNNaN
292GO:0065008regulation of biological qualityFalseNaNNaN337.0NaNNaNNaNNaNNaNNaNNaN
293GO:0000302response to reactive oxygen speciesFalseNaNNaN340.0NaNNaNNaNNaNNaNNaNNaN
294GO:0006950response to stressFalseNaNNaN341.0NaNNaNNaNNaNNaNNaNNaN
295GO:0006979response to oxidative stressFalseNaNNaN342.0NaNNaNNaNNaNNaNNaNNaN
296GO:0033554cellular response to stressFalseNaNNaN343.0NaNNaNNaNNaNNaNNaNNaN
297GO:0034599cellular response to oxidative stressFalseNaNNaN344.0NaNNaNNaNNaNNaNNaNNaN
298GO:0042221response to chemicalFalseNaNNaN346.0NaNNaNNaNNaNNaNNaNNaN
299GO:0050896response to stimulusFalseNaNNaN347.0NaNNaNNaNNaNNaNNaNNaN
300GO:0051716cellular response to stimulusFalseNaNNaN348.0NaNNaNNaNNaNNaNNaNNaN
301GO:0062197cellular response to chemical stressFalseNaNNaN349.0NaNNaNNaNNaNNaNNaNNaN
302GO:0070887cellular response to chemical stimulusFalseNaNNaN350.0NaNNaNNaNNaNNaNNaNNaN
303GO:1901700response to oxygen-containing compoundFalseNaNNaN351.0NaNNaNNaNNaNNaNNaNNaN
304GO:1901701cellular response to oxygen-containing compoundFalseNaNNaN352.0NaNNaNNaNNaNNaNNaNNaN
305GO:0040008regulation of growthFalseNaNNaN353.0NaNNaNNaNNaNNaNNaNNaN
306GO:0040014regulation of multicellular organism growthFalseNaNNaN354.0NaNNaNNaNNaNNaNNaNNaN
307GO:0045927positive regulation of growthFalseNaNNaN356.0NaNNaNNaNNaNNaNNaNNaN
308GO:0048518positive regulation of biological processFalseNaNNaN357.0NaNNaNNaNNaNNaNNaNNaN
309GO:0048638regulation of developmental growthFalseNaNNaN358.0NaNNaNNaNNaNNaNNaNNaN
310GO:0048639positive regulation of developmental growthFalseNaNNaN359.0NaNNaNNaNNaNNaNNaNNaN
311GO:0050793regulation of developmental processFalseNaNNaN360.0NaNNaNNaNNaNNaNNaNNaN
312GO:0051094positive regulation of developmental processFalseNaNNaN361.0NaNNaNNaNNaNNaNNaNNaN
313GO:0051239regulation of multicellular organismal processFalseNaNNaN362.0NaNNaNNaNNaNNaNNaNNaN
314GO:0051240positive regulation of multicellular organismal processFalseNaNNaN363.0NaNNaNNaNNaNNaNNaNNaN
315GO:0042802identical protein bindingFalseNaNNaN364.0NaNNaNNaNNaNNaNNaNNaN
316GO:0046983protein dimerization activityFalseNaNNaN366.0NaNNaNNaNNaNNaNNaNNaN
317GO:0022411cellular component disassemblyFalseNaNNaN369.0NaNNaNNaNNaNNaNNaNNaN
318GO:0032984protein-containing complex disassemblyFalseNaNNaN370.0NaNNaNNaNNaNNaNNaNNaN
319GO:0043933protein-containing complex organizationFalseNaNNaN371.0NaNNaNNaNNaNNaNNaNNaN
320GO:0006612protein targeting to membraneFalseNaNNaN374.0NaNNaNNaNNaNNaNNaNNaN
321GO:0051668localization within membraneFalseNaNNaN376.0NaNNaNNaNNaNNaNNaNNaN
322GO:0072657protein localization to membraneFalseNaNNaN377.0NaNNaNNaNNaNNaNNaNNaN
323GO:0090150establishment of protein localization to membraneFalseNaNNaN378.0NaNNaNNaNNaNNaNNaNNaN
324GO:0008285negative regulation of cell population proliferationFalseNaNNaN379.09.0NaNNaNNaNNaNNaNNaN
325GO:0042127regulation of cell population proliferationFalseNaNNaN380.0NaNNaNNaNNaNNaNNaNNaN
326GO:0048145regulation of fibroblast proliferationFalseNaNNaN381.0NaNNaNNaNNaNNaNNaNNaN
327GO:0050678regulation of epithelial cell proliferationFalseNaNNaN384.0NaNNaNNaNNaNNaNNaNNaN
328GO:0003008system processFalseNaNNaN387.0NaNNaNNaNNaNNaNNaNNaN
329GO:0050877nervous system processFalseNaNNaN388.0NaNNaNNaNNaNNaNNaNNaN
330GO:0007017microtubule-based processFalseNaNNaN390.0NaNNaNNaNNaNNaNNaNNaN
331GO:0051640organelle localizationFalseNaNNaN391.0NaNNaNNaNNaNNaNNaNNaN
332GO:0060151peroxisome localizationFalseNaNNaN392.0NaNNaNNaNNaNNaNNaNNaN
333GO:0004842ubiquitin-protein transferase activityFalseNaNNaN394.0NaNNaNNaNNaNNaNNaNNaN
334GO:0016740transferase activityFalseNaNNaN395.0NaNNaNNaNNaNNaNNaNNaN
335GO:0016746acyltransferase activityFalseNaNNaN396.0NaNNaNNaNNaNNaNNaNNaN
336GO:0016755aminoacyltransferase activityFalseNaNNaN397.0NaNNaNNaNNaNNaNNaNNaN
337GO:0019787ubiquitin-like protein transferase activityFalseNaNNaN398.0NaNNaNNaNNaNNaNNaNNaN
338GO:0061659ubiquitin-like protein ligase activityFalseNaNNaN400.0NaNNaNNaNNaNNaNNaNNaN
339GO:0140096catalytic activity, acting on a proteinFalseNaNNaN401.0NaNNaNNaNNaNNaNNaNNaN
340GO:0005576extracellular regionFalseNaNNaN402.0NaNNaNNaNNaNNaNNaNNaN
341GO:0005615extracellular spaceFalseNaNNaN403.0NaNNaNNaNNaNNaNNaNNaN
342GO:0031982vesicleFalseNaNNaN404.0NaNNaNNaNNaNNaNNaNNaN
343GO:0043230extracellular organelleFalseNaNNaN405.0NaNNaNNaNNaNNaNNaNNaN
344GO:0065010extracellular membrane-bounded organelleFalseNaNNaN406.0NaNNaNNaNNaNNaNNaNNaN
345GO:1903561extracellular vesicleFalseNaNNaN408.0NaNNaNNaNNaNNaNNaNNaN
346GO:0140030modification-dependent protein bindingFalseNaNNaN409.0NaNNaNNaNNaNNaNNaNNaN
347GO:0140104molecular carrier activityFalseNaNNaN412.0NaNNaNNaNNaNNaNNaNNaN
348GO:0009605response to external stimulusFalseNaNNaN414.0NaNNaNNaNNaNNaNNaNNaN
349GO:0009991response to extracellular stimulusFalseNaNNaN415.0NaNNaNNaNNaNNaNNaNNaN
350GO:0031667response to nutrient levelsFalseNaNNaN416.0NaNNaNNaNNaNNaNNaNNaN
351GO:0042594response to starvationFalseNaNNaN417.0NaNNaNNaNNaNNaNNaNNaN
352peroxisome biogenesisNoneFalseNaNNaNNaN0.0NaNNaNNaN0.00.0NaN
353GO:0065005protein-lipid complex assemblyFalseNaNNaNNaN4.0NaNNaNNaNNaNNaNNaN
354GO:0065003protein-containing complex assemblyFalseNaNNaNNaN5.0NaNNaNNaNNaNNaNNaN
355GO:0051262protein tetramerizationFalseNaNNaNNaN6.0NaNNaNNaNNaNNaNNaN
356peroxisome membrane organizationNoneFalseNaNNaNNaNNaN1.0NaNNaNNaNNaNNaN
357protein import into peroxisomeNoneFalseNaNNaNNaNNaN2.01.0NaNNaNNaNNaN
358MONDO:0019234NoneFalseNaNNaNNaNNaNNaN2.0NaNNaNNaNNaN
359peroxisomal biogenesisNoneFalseNaNNaNNaNNaNNaN3.0NaNNaNNaN0.0
360protein-lipid complex formationNoneFalseNaNNaNNaNNaNNaN4.0NaNNaNNaNNaN
361peroxisomal targeting signalNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN1.0NaN
362peroxinsNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN2.0NaN
363peroxisomal matrix proteins importNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN3.0NaN
364mitochondrial metabolismNoneFalseNaNNaNNaNNaNNaNNaNNaN2.0NaNNaN
365cellular transportNoneFalseNaNNaNNaNNaNNaNNaNNaN4.0NaNNaN
366devlopment of membrane structuresNoneFalseNaNNaNNaNNaNNaNNaNNaN5.0NaNNaN
367cell membrane homeostasisNoneFalseNaNNaNNaNNaNNaNNaNNaN6.0NaNNaN
368dna and rna processingNoneFalseNaNNaNNaNNaNNaNNaNNaN7.0NaNNaN
369mitochondrial crop assemblyNoneFalseNaNNaNNaNNaNNaNNaNNaN8.0NaNNaN
370peroxisomal protein importNoneFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0
371membrane vesicle assemblyNoneFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0
372c-terminal tripeptide peroxisomal targeting signal bindingNoneFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN3.0
373GO:0005886plasma membraneFalseNaNNaNNaNNaNNaNNaN5.0NaNNaNNaN
\n", - "
" - ], - "text/plain": [ - " id \\\n", - "0 GO:0006625 \n", - "1 GO:0072663 \n", - "2 GO:0072662 \n", - "3 GO:0015919 \n", - "4 GO:0043574 \n", - "5 GO:0007031 \n", - "6 GO:0016558 \n", - "7 GO:0005778 \n", - "8 GO:0031903 \n", - "9 GO:0016562 \n", - "10 GO:0044743 \n", - "11 GO:0005777 \n", - "12 GO:0042579 \n", - "13 GO:0065002 \n", - "14 GO:0071806 \n", - "15 GO:0006605 \n", - "16 GO:0001881 \n", - "17 GO:0072594 \n", - "18 GO:0043112 \n", - "19 GO:0006886 \n", - "20 GO:0033365 \n", - "21 GO:0015031 \n", - "22 GO:0045184 \n", - "23 GO:0046907 \n", - "24 GO:0140318 \n", - "25 GO:0071705 \n", - "26 GO:0006635 \n", - "27 GO:0051649 \n", - "28 GO:0044721 \n", - "29 GO:0000268 \n", - "30 GO:0043335 \n", - "31 GO:0008104 \n", - "32 GO:0070727 \n", - "33 GO:0045046 \n", - "34 GO:0000425 \n", - "35 GO:0019395 \n", - "36 GO:0034440 \n", - "37 GO:0098588 \n", - "38 GO:0009062 \n", - "39 GO:0071702 \n", - "40 GO:0033036 \n", - "41 GO:0030242 \n", - "42 GO:0072329 \n", - "43 GO:0051641 \n", - "44 GO:0055085 \n", - "45 GO:0140036 \n", - "46 GO:0006996 \n", - "47 GO:0140035 \n", - "48 GO:0030258 \n", - "49 GO:0044242 \n", - "50 GO:0046395 \n", - "51 GO:0016054 \n", - "52 GO:0000038 \n", - "53 GO:0031090 \n", - "54 GO:0006810 \n", - "55 GO:0016042 \n", - "56 GO:0051234 \n", - "57 GO:0006631 \n", - "58 GO:0005048 \n", - "59 GO:0005782 \n", - "60 GO:0031907 \n", - "61 GO:0044282 \n", - "62 GO:0034614 \n", - "63 GO:0001764 \n", - "64 GO:0033328 \n", - "65 GO:0032994 \n", - "66 GO:0005052 \n", - "67 GO:0060152 \n", - "68 GO:0005053 \n", - "69 GO:0005829 \n", - "70 GO:0032991 \n", - "71 GO:0050680 \n", - "72 GO:0031648 \n", - "73 GO:0019899 \n", - "74 GO:0008611 \n", - "75 GO:0048468 \n", - "76 GO:0070062 \n", - "77 GO:0044183 \n", - "78 GO:0040018 \n", - "79 GO:0140597 \n", - "80 GO:0030674 \n", - "81 GO:0016561 \n", - "82 GO:0005654 \n", - "83 GO:0005737 \n", - "84 GO:0050821 \n", - "85 GO:0008289 \n", - "86 GO:0021795 \n", - "87 GO:0031333 \n", - "88 GO:0001958 \n", - "89 GO:0016560 \n", - "90 GO:0005524 \n", - "91 GO:0061630 \n", - "92 GO:0016887 \n", - "93 GO:0007006 \n", - "94 GO:0008270 \n", - "95 GO:0097733 \n", - "96 GO:0007029 \n", - "97 GO:0005783 \n", - "98 GO:1990928 \n", - "99 GO:0042803 \n", - "100 GO:0016593 \n", - "101 GO:0048147 \n", - "102 GO:0044877 \n", - "103 GO:0008320 \n", - "104 GO:0005739 \n", - "105 GO:0005794 \n", - "106 GO:0001750 \n", - "107 GO:0016020 \n", - "108 GO:0006513 \n", - "109 GO:0006457 \n", - "110 GO:0021895 \n", - "111 GO:0050905 \n", - "112 GO:0031267 \n", - "113 BFO:0000003 \n", - "114 BFO:0000015 \n", - "115 GO:0006082 \n", - "116 GO:0006629 \n", - "117 GO:0008150 \n", - "118 GO:0008152 \n", - "119 GO:0009987 \n", - "120 GO:0019752 \n", - "121 GO:0032787 \n", - "122 GO:0043436 \n", - "123 GO:0044237 \n", - "124 GO:0044238 \n", - "125 GO:0044255 \n", - "126 GO:0044281 \n", - "127 GO:0071704 \n", - "128 GO:0003674 \n", - "129 GO:0005488 \n", - "130 GO:0033218 \n", - "131 GO:0042277 \n", - "132 GO:0006914 \n", - "133 GO:0009056 \n", - "134 GO:0016236 \n", - "135 GO:0044248 \n", - "136 GO:0061912 \n", - "137 GO:0061919 \n", - "138 BFO:0000002 \n", - "139 BFO:0000004 \n", - "140 BFO:0000040 \n", - "141 CARO:0000000 \n", - "142 CARO:0000003 \n", - "143 CARO:0000006 \n", - "144 CARO:0030000 \n", - "145 CL:0000000 \n", - "146 CL:0000003 \n", - "147 CL:0000006 \n", - "148 CL:0000101 \n", - "149 CL:0000197 \n", - "150 CL:0000210 \n", - "151 CL:0000211 \n", - "152 CL:0000255 \n", - "153 CL:0000393 \n", - "154 CL:0000404 \n", - "155 CL:0000526 \n", - "156 CL:0000540 \n", - "157 CL:0000548 \n", - "158 CL:0002319 \n", - "159 CL:0002371 \n", - "160 GO:0005575 \n", - "161 GO:0005929 \n", - "162 GO:0042995 \n", - "163 GO:0043005 \n", - "164 GO:0043226 \n", - "165 GO:0043227 \n", - "166 GO:0097730 \n", - "167 GO:0097731 \n", - "168 GO:0110165 \n", - "169 GO:0120025 \n", - "170 PR:000050567 \n", - "171 UBERON:0000061 \n", - "172 UBERON:0000465 \n", - "173 UBERON:0001062 \n", - "174 GO:0007275 \n", - "175 GO:0007399 \n", - "176 GO:0022008 \n", - "177 GO:0030154 \n", - "178 GO:0032501 \n", - "179 GO:0032502 \n", - "180 GO:0048699 \n", - "181 GO:0048731 \n", - "182 GO:0048856 \n", - "183 GO:0048869 \n", - "184 GO:0016477 \n", - "185 GO:0048870 \n", - "186 GO:0001501 \n", - "187 GO:0009653 \n", - "188 GO:0009887 \n", - "189 GO:0048513 \n", - "190 GO:0048705 \n", - "191 GO:0060348 \n", - "192 GO:0060349 \n", - "193 GO:0060350 \n", - "194 GO:0001503 \n", - "195 GO:0036075 \n", - "196 GO:0000166 \n", - "197 GO:0017076 \n", - "198 GO:0030554 \n", - "199 GO:0032553 \n", - "200 GO:0032555 \n", - "201 GO:0032559 \n", - "202 GO:0035639 \n", - "203 GO:0036094 \n", - "204 GO:0043167 \n", - "205 GO:0043168 \n", - "206 GO:0097159 \n", - "207 GO:0097367 \n", - "208 GO:1901265 \n", - "209 GO:1901363 \n", - "210 GO:0005622 \n", - "211 GO:0005634 \n", - "212 GO:0031974 \n", - "213 GO:0031981 \n", - "214 GO:0043229 \n", - "215 GO:0043231 \n", - "216 GO:0043233 \n", - "217 GO:0070013 \n", - "218 GO:0012505 \n", - "219 GO:0006807 \n", - "220 GO:0016567 \n", - "221 GO:0019538 \n", - "222 GO:0032446 \n", - "223 GO:0036211 \n", - "224 GO:0043170 \n", - "225 GO:0043412 \n", - "226 GO:0070647 \n", - "227 GO:1901564 \n", - "228 GO:0051179 \n", - "229 GO:0016043 \n", - "230 GO:0071840 \n", - "231 GO:1901575 \n", - "232 GO:0007005 \n", - "233 GO:0061024 \n", - "234 GO:0010256 \n", - "235 GO:0043169 \n", - "236 GO:0046872 \n", - "237 GO:0046914 \n", - "238 GO:0005215 \n", - "239 GO:0022857 \n", - "240 GO:0022884 \n", - "241 GO:0006662 \n", - "242 GO:0008610 \n", - "243 GO:0009058 \n", - "244 GO:0018904 \n", - "245 GO:0044249 \n", - "246 GO:0046485 \n", - "247 GO:0046504 \n", - "248 GO:0097384 \n", - "249 GO:1901503 \n", - "250 GO:1901576 \n", - "251 GO:0000428 \n", - "252 GO:0016591 \n", - "253 GO:0030880 \n", - "254 GO:0055029 \n", - "255 GO:0061695 \n", - "256 GO:0140513 \n", - "257 GO:0140535 \n", - "258 GO:1902494 \n", - "259 GO:1990234 \n", - "260 GO:0008023 \n", - "261 GO:0140657 \n", - "262 GO:0003824 \n", - "263 GO:0016462 \n", - "264 GO:0016787 \n", - "265 GO:0016817 \n", - "266 GO:0016818 \n", - "267 GO:0017111 \n", - "268 GO:0005515 \n", - "269 GO:0007417 \n", - "270 GO:0007420 \n", - "271 GO:0021537 \n", - "272 GO:0021543 \n", - "273 GO:0021987 \n", - "274 GO:0030900 \n", - "275 GO:0060322 \n", - "276 GO:0021885 \n", - "277 GO:0022029 \n", - "278 GO:0021953 \n", - "279 GO:0030182 \n", - "280 GO:0060090 \n", - "281 GO:0051020 \n", - "282 GO:0043254 \n", - "283 GO:0044087 \n", - "284 GO:0048519 \n", - "285 GO:0048523 \n", - "286 GO:0050789 \n", - "287 GO:0050794 \n", - "288 GO:0051128 \n", - "289 GO:0051129 \n", - "290 GO:0065007 \n", - "291 GO:0031647 \n", - "292 GO:0065008 \n", - "293 GO:0000302 \n", - "294 GO:0006950 \n", - "295 GO:0006979 \n", - "296 GO:0033554 \n", - "297 GO:0034599 \n", - "298 GO:0042221 \n", - "299 GO:0050896 \n", - "300 GO:0051716 \n", - "301 GO:0062197 \n", - "302 GO:0070887 \n", - "303 GO:1901700 \n", - "304 GO:1901701 \n", - "305 GO:0040008 \n", - "306 GO:0040014 \n", - "307 GO:0045927 \n", - "308 GO:0048518 \n", - "309 GO:0048638 \n", - "310 GO:0048639 \n", - "311 GO:0050793 \n", - "312 GO:0051094 \n", - "313 GO:0051239 \n", - "314 GO:0051240 \n", - "315 GO:0042802 \n", - "316 GO:0046983 \n", - "317 GO:0022411 \n", - "318 GO:0032984 \n", - "319 GO:0043933 \n", - "320 GO:0006612 \n", - "321 GO:0051668 \n", - "322 GO:0072657 \n", - "323 GO:0090150 \n", - "324 GO:0008285 \n", - "325 GO:0042127 \n", - "326 GO:0048145 \n", - "327 GO:0050678 \n", - "328 GO:0003008 \n", - "329 GO:0050877 \n", - "330 GO:0007017 \n", - "331 GO:0051640 \n", - "332 GO:0060151 \n", - "333 GO:0004842 \n", - "334 GO:0016740 \n", - "335 GO:0016746 \n", - "336 GO:0016755 \n", - "337 GO:0019787 \n", - "338 GO:0061659 \n", - "339 GO:0140096 \n", - "340 GO:0005576 \n", - "341 GO:0005615 \n", - "342 GO:0031982 \n", - "343 GO:0043230 \n", - "344 GO:0065010 \n", - "345 GO:1903561 \n", - "346 GO:0140030 \n", - "347 GO:0140104 \n", - "348 GO:0009605 \n", - "349 GO:0009991 \n", - "350 GO:0031667 \n", - "351 GO:0042594 \n", - "352 peroxisome biogenesis \n", - "353 GO:0065005 \n", - "354 GO:0065003 \n", - "355 GO:0051262 \n", - "356 peroxisome membrane organization \n", - "357 protein import into peroxisome \n", - "358 MONDO:0019234 \n", - "359 peroxisomal biogenesis \n", - "360 protein-lipid complex formation \n", - "361 peroxisomal targeting signal \n", - "362 peroxins \n", - "363 peroxisomal matrix proteins import \n", - "364 mitochondrial metabolism \n", - "365 cellular transport \n", - "366 devlopment of membrane structures \n", - "367 cell membrane homeostasis \n", - "368 dna and rna processing \n", - "369 mitochondrial crop assembly \n", - "370 peroxisomal protein import \n", - "371 membrane vesicle assembly \n", - "372 c-terminal tripeptide peroxisomal targeting signal binding \n", - "373 GO:0005886 \n", - "\n", - " label \\\n", - "0 protein targeting to peroxisome \n", - "1 establishment of protein localization to peroxisome \n", - "2 protein localization to peroxisome \n", - "3 peroxisomal membrane transport \n", - "4 peroxisomal transport \n", - "5 peroxisome organization \n", - "6 protein import into peroxisome matrix \n", - "7 peroxisomal membrane \n", - "8 microbody membrane \n", - "9 protein import into peroxisome matrix, receptor recycling \n", - "10 protein transmembrane import into intracellular organelle \n", - "11 peroxisome \n", - "12 microbody \n", - "13 intracellular protein transmembrane transport \n", - "14 protein transmembrane transport \n", - "15 protein targeting \n", - "16 receptor recycling \n", - "17 establishment of protein localization to organelle \n", - "18 receptor metabolic process \n", - "19 intracellular protein transport \n", - "20 protein localization to organelle \n", - "21 protein transport \n", - "22 establishment of protein localization \n", - "23 intracellular transport \n", - "24 protein transporter activity \n", - "25 nitrogen compound transport \n", - "26 fatty acid beta-oxidation \n", - "27 establishment of localization in cell \n", - "28 protein import into peroxisome matrix, substrate release \n", - "29 peroxisome targeting sequence binding \n", - "30 protein unfolding \n", - "31 protein localization \n", - "32 cellular macromolecule localization \n", - "33 protein import into peroxisome membrane \n", - "34 pexophagy \n", - "35 fatty acid oxidation \n", - "36 lipid oxidation \n", - "37 bounding membrane of organelle \n", - "38 fatty acid catabolic process \n", - "39 organic substance transport \n", - "40 macromolecule localization \n", - "41 autophagy of peroxisome \n", - "42 monocarboxylic acid catabolic process \n", - "43 cellular localization \n", - "44 transmembrane transport \n", - "45 ubiquitin-dependent protein binding \n", - "46 organelle organization \n", - "47 ubiquitination-like modification-dependent protein binding \n", - "48 lipid modification \n", - "49 cellular lipid catabolic process \n", - "50 carboxylic acid catabolic process \n", - "51 organic acid catabolic process \n", - "52 very long-chain fatty acid metabolic process \n", - "53 organelle membrane \n", - "54 transport \n", - "55 lipid catabolic process \n", - "56 establishment of localization \n", - "57 fatty acid metabolic process \n", - "58 signal sequence binding \n", - "59 peroxisomal matrix \n", - "60 microbody lumen \n", - "61 small molecule catabolic process \n", - "62 cellular response to reactive oxygen species \n", - "63 neuron migration \n", - "64 peroxisome membrane targeting sequence binding \n", - "65 protein-lipid complex \n", - "66 peroxisome matrix targeting signal-1 binding \n", - "67 microtubule-based peroxisome localization \n", - "68 peroxisome matrix targeting signal-2 binding \n", - "69 cytosol \n", - "70 protein-containing complex \n", - "71 negative regulation of epithelial cell proliferation \n", - "72 protein destabilization \n", - "73 enzyme binding \n", - "74 ether lipid biosynthetic process \n", - "75 cell development \n", - "76 extracellular exosome \n", - "77 protein folding chaperone \n", - "78 positive regulation of multicellular organism growth \n", - "79 protein carrier chaperone \n", - "80 protein-macromolecule adaptor activity \n", - "81 protein import into peroxisome matrix, translocation \n", - "82 nucleoplasm \n", - "83 cytoplasm \n", - "84 protein stabilization \n", - "85 lipid binding \n", - "86 cerebral cortex cell migration \n", - "87 negative regulation of protein-containing complex assembly \n", - "88 endochondral ossification \n", - "89 protein import into peroxisome matrix, docking \n", - "90 ATP binding \n", - "91 ubiquitin protein ligase activity \n", - "92 ATP hydrolysis activity \n", - "93 mitochondrial membrane organization \n", - "94 zinc ion binding \n", - "95 photoreceptor cell cilium \n", - "96 endoplasmic reticulum organization \n", - "97 endoplasmic reticulum \n", - "98 response to amino acid starvation \n", - "99 protein homodimerization activity \n", - "100 Cdc73/Paf1 complex \n", - "101 negative regulation of fibroblast proliferation \n", - "102 protein-containing complex binding \n", - "103 protein transmembrane transporter activity \n", - "104 mitochondrion \n", - "105 Golgi apparatus \n", - "106 photoreceptor outer segment \n", - "107 membrane \n", - "108 protein monoubiquitination \n", - "109 protein folding \n", - "110 cerebral cortex neuron differentiation \n", - "111 neuromuscular process \n", - "112 small GTPase binding \n", - "113 occurrent \n", - "114 process \n", - "115 organic acid metabolic process \n", - "116 lipid metabolic process \n", - "117 biological_process \n", - "118 metabolic process \n", - "119 cellular process \n", - "120 carboxylic acid metabolic process \n", - "121 monocarboxylic acid metabolic process \n", - "122 oxoacid metabolic process \n", - "123 cellular metabolic process \n", - "124 primary metabolic process \n", - "125 cellular lipid metabolic process \n", - "126 small molecule metabolic process \n", - "127 organic substance metabolic process \n", - "128 molecular_function \n", - "129 binding \n", - "130 amide binding \n", - "131 peptide binding \n", - "132 autophagy \n", - "133 catabolic process \n", - "134 macroautophagy \n", - "135 cellular catabolic process \n", - "136 selective autophagy \n", - "137 process utilizing autophagic mechanism \n", - "138 continuant \n", - "139 independent continuant \n", - "140 material entity \n", - "141 anatomical entity \n", - "142 connected anatomical structure \n", - "143 material anatomical entity \n", - "144 biological entity \n", - "145 cell \n", - "146 native cell \n", - "147 neuronal receptor cell \n", - "148 sensory neuron \n", - "149 sensory receptor cell \n", - "150 photoreceptor cell \n", - "151 electrically active cell \n", - "152 eukaryotic cell \n", - "153 electrically responsive cell \n", - "154 electrically signaling cell \n", - "155 afferent neuron \n", - "156 neuron \n", - "157 animal cell \n", - "158 neural cell \n", - "159 somatic cell \n", - "160 cellular_component \n", - "161 cilium \n", - "162 cell projection \n", - "163 neuron projection \n", - "164 organelle \n", - "165 membrane-bounded organelle \n", - "166 non-motile cilium \n", - "167 9+0 non-motile cilium \n", - "168 cellular anatomical entity \n", - "169 plasma membrane bounded cell projection \n", - "170 protein-containing material entity \n", - "171 anatomical structure \n", - "172 material anatomical entity \n", - "173 anatomical entity \n", - "174 multicellular organism development \n", - "175 nervous system development \n", - "176 neurogenesis \n", - "177 cell differentiation \n", - "178 multicellular organismal process \n", - "179 developmental process \n", - "180 generation of neurons \n", - "181 system development \n", - "182 anatomical structure development \n", - "183 cellular developmental process \n", - "184 cell migration \n", - "185 cell motility \n", - "186 skeletal system development \n", - "187 anatomical structure morphogenesis \n", - "188 animal organ morphogenesis \n", - "189 animal organ development \n", - "190 skeletal system morphogenesis \n", - "191 bone development \n", - "192 bone morphogenesis \n", - "193 endochondral bone morphogenesis \n", - "194 ossification \n", - "195 replacement ossification \n", - "196 nucleotide binding \n", - "197 purine nucleotide binding \n", - "198 adenyl nucleotide binding \n", - "199 ribonucleotide binding \n", - "200 purine ribonucleotide binding \n", - "201 adenyl ribonucleotide binding \n", - "202 purine ribonucleoside triphosphate binding \n", - "203 small molecule binding \n", - "204 ion binding \n", - "205 anion binding \n", - "206 organic cyclic compound binding \n", - "207 carbohydrate derivative binding \n", - "208 nucleoside phosphate binding \n", - "209 heterocyclic compound binding \n", - "210 intracellular anatomical structure \n", - "211 nucleus \n", - "212 membrane-enclosed lumen \n", - "213 nuclear lumen \n", - "214 intracellular organelle \n", - "215 intracellular membrane-bounded organelle \n", - "216 organelle lumen \n", - "217 intracellular organelle lumen \n", - "218 endomembrane system \n", - "219 nitrogen compound metabolic process \n", - "220 protein ubiquitination \n", - "221 protein metabolic process \n", - "222 protein modification by small protein conjugation \n", - "223 protein modification process \n", - "224 macromolecule metabolic process \n", - "225 macromolecule modification \n", - "226 protein modification by small protein conjugation or removal \n", - "227 organonitrogen compound metabolic process \n", - "228 localization \n", - "229 cellular component organization \n", - "230 cellular component organization or biogenesis \n", - "231 organic substance catabolic process \n", - "232 mitochondrion organization \n", - "233 membrane organization \n", - "234 endomembrane system organization \n", - "235 cation binding \n", - "236 metal ion binding \n", - "237 transition metal ion binding \n", - "238 transporter activity \n", - "239 transmembrane transporter activity \n", - "240 macromolecule transmembrane transporter activity \n", - "241 glycerol ether metabolic process \n", - "242 lipid biosynthetic process \n", - "243 biosynthetic process \n", - "244 ether metabolic process \n", - "245 cellular biosynthetic process \n", - "246 ether lipid metabolic process \n", - "247 glycerol ether biosynthetic process \n", - "248 cellular lipid biosynthetic process \n", - "249 ether biosynthetic process \n", - "250 organic substance biosynthetic process \n", - "251 DNA-directed RNA polymerase complex \n", - "252 RNA polymerase II, holoenzyme \n", - "253 RNA polymerase complex \n", - "254 nuclear DNA-directed RNA polymerase complex \n", - "255 transferase complex, transferring phosphorus-containing groups \n", - "256 nuclear protein-containing complex \n", - "257 intracellular protein-containing complex \n", - "258 catalytic complex \n", - "259 transferase complex \n", - "260 transcription elongation factor complex \n", - "261 ATP-dependent activity \n", - "262 catalytic activity \n", - "263 pyrophosphatase activity \n", - "264 hydrolase activity \n", - "265 hydrolase activity, acting on acid anhydrides \n", - "266 hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides \n", - "267 ribonucleoside triphosphate phosphatase activity \n", - "268 protein binding \n", - "269 central nervous system development \n", - "270 brain development \n", - "271 telencephalon development \n", - "272 pallium development \n", - "273 cerebral cortex development \n", - "274 forebrain development \n", - "275 head development \n", - "276 forebrain cell migration \n", - "277 telencephalon cell migration \n", - "278 central nervous system neuron differentiation \n", - "279 neuron differentiation \n", - "280 molecular adaptor activity \n", - "281 GTPase binding \n", - "282 regulation of protein-containing complex assembly \n", - "283 regulation of cellular component biogenesis \n", - "284 negative regulation of biological process \n", - "285 negative regulation of cellular process \n", - "286 regulation of biological process \n", - "287 regulation of cellular process \n", - "288 regulation of cellular component organization \n", - "289 negative regulation of cellular component organization \n", - "290 biological regulation \n", - "291 regulation of protein stability \n", - "292 regulation of biological quality \n", - "293 response to reactive oxygen species \n", - "294 response to stress \n", - "295 response to oxidative stress \n", - "296 cellular response to stress \n", - "297 cellular response to oxidative stress \n", - "298 response to chemical \n", - "299 response to stimulus \n", - "300 cellular response to stimulus \n", - "301 cellular response to chemical stress \n", - "302 cellular response to chemical stimulus \n", - "303 response to oxygen-containing compound \n", - "304 cellular response to oxygen-containing compound \n", - "305 regulation of growth \n", - "306 regulation of multicellular organism growth \n", - "307 positive regulation of growth \n", - "308 positive regulation of biological process \n", - "309 regulation of developmental growth \n", - "310 positive regulation of developmental growth \n", - "311 regulation of developmental process \n", - "312 positive regulation of developmental process \n", - "313 regulation of multicellular organismal process \n", - "314 positive regulation of multicellular organismal process \n", - "315 identical protein binding \n", - "316 protein dimerization activity \n", - "317 cellular component disassembly \n", - "318 protein-containing complex disassembly \n", - "319 protein-containing complex organization \n", - "320 protein targeting to membrane \n", - "321 localization within membrane \n", - "322 protein localization to membrane \n", - "323 establishment of protein localization to membrane \n", - "324 negative regulation of cell population proliferation \n", - "325 regulation of cell population proliferation \n", - "326 regulation of fibroblast proliferation \n", - "327 regulation of epithelial cell proliferation \n", - "328 system process \n", - "329 nervous system process \n", - "330 microtubule-based process \n", - "331 organelle localization \n", - "332 peroxisome localization \n", - "333 ubiquitin-protein transferase activity \n", - "334 transferase activity \n", - "335 acyltransferase activity \n", - "336 aminoacyltransferase activity \n", - "337 ubiquitin-like protein transferase activity \n", - "338 ubiquitin-like protein ligase activity \n", - "339 catalytic activity, acting on a protein \n", - "340 extracellular region \n", - "341 extracellular space \n", - "342 vesicle \n", - "343 extracellular organelle \n", - "344 extracellular membrane-bounded organelle \n", - "345 extracellular vesicle \n", - "346 modification-dependent protein binding \n", - "347 molecular carrier activity \n", - "348 response to external stimulus \n", - "349 response to extracellular stimulus \n", - "350 response to nutrient levels \n", - "351 response to starvation \n", - "352 None \n", - "353 protein-lipid complex assembly \n", - "354 protein-containing complex assembly \n", - "355 protein tetramerization \n", - "356 None \n", - "357 None \n", - "358 None \n", - "359 None \n", - "360 None \n", - "361 None \n", - "362 None \n", - "363 None \n", - "364 None \n", - "365 None \n", - "366 None \n", - "367 None \n", - "368 None \n", - "369 None \n", - "370 None \n", - "371 None \n", - "372 None \n", - "373 plasma membrane \n", - "\n", - " redundant standard standard no ontology None \\\n", - "0 False 0.0 10.0 218.0 \n", - "1 True 1.0 NaN 236.0 \n", - "2 True 2.0 NaN 235.0 \n", - "3 False 3.0 NaN 277.0 \n", - "4 True 4.0 NaN 226.0 \n", - "5 True 5.0 2.0 222.0 \n", - "6 True 6.0 0.0 278.0 \n", - "7 False 7.0 1.0 190.0 \n", - "8 True 8.0 NaN 193.0 \n", - "9 True 9.0 3.0 284.0 \n", - "10 True 10.0 NaN 279.0 \n", - "11 True 11.0 4.0 188.0 \n", - "12 True 12.0 NaN 189.0 \n", - "13 True 13.0 NaN 280.0 \n", - "14 True 14.0 NaN 260.0 \n", - "15 True 15.0 NaN 217.0 \n", - "16 True 16.0 NaN 283.0 \n", - "17 True 17.0 NaN 234.0 \n", - "18 True 18.0 NaN 285.0 \n", - "19 True 19.0 NaN 220.0 \n", - "20 True 20.0 NaN 225.0 \n", - "21 True 21.0 NaN 223.0 \n", - "22 True 22.0 NaN 227.0 \n", - "23 True 23.0 NaN 228.0 \n", - "24 False 24.0 11.0 265.0 \n", - "25 True 25.0 NaN 232.0 \n", - "26 False 26.0 5.0 237.0 \n", - "27 True 27.0 NaN 230.0 \n", - "28 True 28.0 7.0 372.0 \n", - "29 False 29.0 NaN 84.0 \n", - "30 False 30.0 6.0 367.0 \n", - "31 True 31.0 NaN 212.0 \n", - "32 True 32.0 NaN 216.0 \n", - "33 True 33.0 9.0 375.0 \n", - "34 False 34.0 8.0 90.0 \n", - "35 True 35.0 NaN 241.0 \n", - "36 True 36.0 NaN 243.0 \n", - "37 True 37.0 NaN 194.0 \n", - "38 True 38.0 NaN 238.0 \n", - "39 True 39.0 NaN 231.0 \n", - "40 True 40.0 NaN 213.0 \n", - "41 True 41.0 NaN 94.0 \n", - "42 True 42.0 NaN 247.0 \n", - "43 True 43.0 NaN 215.0 \n", - "44 True 44.0 NaN 259.0 \n", - "45 False 45.0 12.0 411.0 \n", - "46 True 46.0 NaN 221.0 \n", - "47 True 47.0 NaN 410.0 \n", - "48 True 48.0 NaN 242.0 \n", - "49 True 49.0 NaN 244.0 \n", - "50 True 50.0 NaN 246.0 \n", - "51 True 51.0 NaN 240.0 \n", - "52 False 52.0 13.0 69.0 \n", - "53 True 53.0 NaN 192.0 \n", - "54 True 54.0 NaN 219.0 \n", - "55 True 55.0 NaN 239.0 \n", - "56 True 56.0 NaN 229.0 \n", - "57 True 57.0 NaN 72.0 \n", - "58 True 58.0 NaN 86.0 \n", - "59 True 59.0 15.0 195.0 \n", - "60 True 60.0 NaN 196.0 \n", - "61 True 61.0 NaN 245.0 \n", - "62 False NaN 14.0 345.0 \n", - "63 False NaN 16.0 146.0 \n", - "64 False NaN 17.0 339.0 \n", - "65 False NaN 18.0 338.0 \n", - "66 False NaN 19.0 160.0 \n", - "67 False NaN 20.0 393.0 \n", - "68 False NaN NaN 161.0 \n", - "69 False NaN NaN 200.0 \n", - "70 False NaN NaN 289.0 \n", - "71 False NaN NaN 385.0 \n", - "72 False NaN NaN 336.0 \n", - "73 False NaN NaN 307.0 \n", - "74 False NaN NaN 268.0 \n", - "75 False NaN NaN 383.0 \n", - "76 False NaN NaN 407.0 \n", - "77 False NaN NaN 368.0 \n", - "78 False NaN NaN 355.0 \n", - "79 False NaN NaN 413.0 \n", - "80 False NaN NaN 321.0 \n", - "81 False NaN NaN 282.0 \n", - "82 False NaN NaN 185.0 \n", - "83 False NaN NaN 186.0 \n", - "84 False NaN NaN 386.0 \n", - "85 False NaN NaN 258.0 \n", - "86 False NaN NaN 315.0 \n", - "87 False NaN NaN 325.0 \n", - "88 False NaN NaN 158.0 \n", - "89 False NaN NaN 281.0 \n", - "90 False NaN NaN 163.0 \n", - "91 False NaN NaN 399.0 \n", - "92 False NaN NaN 304.0 \n", - "93 False NaN NaN 250.0 \n", - "94 False NaN NaN 254.0 \n", - "95 False NaN NaN 128.0 \n", - "96 False NaN NaN 253.0 \n", - "97 False NaN NaN 198.0 \n", - "98 False NaN NaN 418.0 \n", - "99 False NaN NaN 365.0 \n", - "100 False NaN NaN 297.0 \n", - "101 False NaN NaN 382.0 \n", - "102 False NaN NaN 373.0 \n", - "103 False NaN NaN 262.0 \n", - "104 False NaN NaN 187.0 \n", - "105 False NaN NaN 199.0 \n", - "106 False NaN NaN 135.0 \n", - "107 False NaN NaN 191.0 \n", - "108 False NaN NaN 202.0 \n", - "109 False NaN NaN 201.0 \n", - "110 False NaN NaN 318.0 \n", - "111 False NaN NaN 389.0 \n", - "112 False NaN NaN 323.0 \n", - "113 False NaN NaN 67.0 \n", - "114 False NaN NaN 68.0 \n", - "115 False NaN NaN 70.0 \n", - "116 False NaN NaN 71.0 \n", - "117 False NaN NaN 73.0 \n", - "118 False NaN NaN 74.0 \n", - "119 False NaN NaN 75.0 \n", - "120 False NaN NaN 76.0 \n", - "121 False NaN NaN 77.0 \n", - "122 False NaN NaN 78.0 \n", - "123 False NaN NaN 79.0 \n", - "124 False NaN NaN 80.0 \n", - "125 False NaN NaN 81.0 \n", - "126 False NaN NaN 82.0 \n", - "127 False NaN NaN 83.0 \n", - "128 False NaN NaN 85.0 \n", - "129 False NaN NaN 87.0 \n", - "130 False NaN NaN 88.0 \n", - "131 False NaN NaN 89.0 \n", - "132 False NaN NaN 91.0 \n", - "133 False NaN NaN 92.0 \n", - "134 False NaN NaN 93.0 \n", - "135 False NaN NaN 95.0 \n", - "136 False NaN NaN 96.0 \n", - "137 False NaN NaN 97.0 \n", - "138 False NaN NaN 98.0 \n", - "139 False NaN NaN 99.0 \n", - "140 False NaN NaN 100.0 \n", - "141 False NaN NaN 101.0 \n", - "142 False NaN NaN 102.0 \n", - "143 False NaN NaN 103.0 \n", - "144 False NaN NaN 104.0 \n", - "145 False NaN NaN 105.0 \n", - "146 False NaN NaN 106.0 \n", - "147 False NaN NaN 107.0 \n", - "148 False NaN NaN 108.0 \n", - "149 False NaN NaN 109.0 \n", - "150 False NaN NaN 110.0 \n", - "151 False NaN NaN 111.0 \n", - "152 False NaN NaN 112.0 \n", - "153 False NaN NaN 113.0 \n", - "154 False NaN NaN 114.0 \n", - "155 False NaN NaN 115.0 \n", - "156 False NaN NaN 116.0 \n", - "157 False NaN NaN 117.0 \n", - "158 False NaN NaN 118.0 \n", - "159 False NaN NaN 119.0 \n", - "160 False NaN NaN 120.0 \n", - "161 False NaN NaN 121.0 \n", - "162 False NaN NaN 122.0 \n", - "163 False NaN NaN 123.0 \n", - "164 False NaN NaN 124.0 \n", - "165 False NaN NaN 125.0 \n", - "166 False NaN NaN 126.0 \n", - "167 False NaN NaN 127.0 \n", - "168 False NaN NaN 129.0 \n", - "169 False NaN NaN 130.0 \n", - "170 False NaN NaN 131.0 \n", - "171 False NaN NaN 132.0 \n", - "172 False NaN NaN 133.0 \n", - "173 False NaN NaN 134.0 \n", - "174 False NaN NaN 136.0 \n", - "175 False NaN NaN 137.0 \n", - "176 False NaN NaN 138.0 \n", - "177 False NaN NaN 139.0 \n", - "178 False NaN NaN 140.0 \n", - "179 False NaN NaN 141.0 \n", - "180 False NaN NaN 142.0 \n", - "181 False NaN NaN 143.0 \n", - "182 False NaN NaN 144.0 \n", - "183 False NaN NaN 145.0 \n", - "184 False NaN NaN 147.0 \n", - "185 False NaN NaN 148.0 \n", - "186 False NaN NaN 149.0 \n", - "187 False NaN NaN 150.0 \n", - "188 False NaN NaN 151.0 \n", - "189 False NaN NaN 152.0 \n", - "190 False NaN NaN 153.0 \n", - "191 False NaN NaN 154.0 \n", - "192 False NaN NaN 155.0 \n", - "193 False NaN NaN 156.0 \n", - "194 False NaN NaN 157.0 \n", - "195 False NaN NaN 159.0 \n", - "196 False NaN NaN 162.0 \n", - "197 False NaN NaN 164.0 \n", - "198 False NaN NaN 165.0 \n", - "199 False NaN NaN 166.0 \n", - "200 False NaN NaN 167.0 \n", - "201 False NaN NaN 168.0 \n", - "202 False NaN NaN 169.0 \n", - "203 False NaN NaN 170.0 \n", - "204 False NaN NaN 171.0 \n", - "205 False NaN NaN 172.0 \n", - "206 False NaN NaN 173.0 \n", - "207 False NaN NaN 174.0 \n", - "208 False NaN NaN 175.0 \n", - "209 False NaN NaN 176.0 \n", - "210 False NaN NaN 177.0 \n", - "211 False NaN NaN 178.0 \n", - "212 False NaN NaN 179.0 \n", - "213 False NaN NaN 180.0 \n", - "214 False NaN NaN 181.0 \n", - "215 False NaN NaN 182.0 \n", - "216 False NaN NaN 183.0 \n", - "217 False NaN NaN 184.0 \n", - "218 False NaN NaN 197.0 \n", - "219 False NaN NaN 203.0 \n", - "220 False NaN NaN 204.0 \n", - "221 False NaN NaN 205.0 \n", - "222 False NaN NaN 206.0 \n", - "223 False NaN NaN 207.0 \n", - "224 False NaN NaN 208.0 \n", - "225 False NaN NaN 209.0 \n", - "226 False NaN NaN 210.0 \n", - "227 False NaN NaN 211.0 \n", - "228 False NaN NaN 214.0 \n", - "229 False NaN NaN 224.0 \n", - "230 False NaN NaN 233.0 \n", - "231 False NaN NaN 248.0 \n", - "232 False NaN NaN 249.0 \n", - "233 False NaN NaN 251.0 \n", - "234 False NaN NaN 252.0 \n", - "235 False NaN NaN 255.0 \n", - "236 False NaN NaN 256.0 \n", - "237 False NaN NaN 257.0 \n", - "238 False NaN NaN 261.0 \n", - "239 False NaN NaN 263.0 \n", - "240 False NaN NaN 264.0 \n", - "241 False NaN NaN 266.0 \n", - "242 False NaN NaN 267.0 \n", - "243 False NaN NaN 269.0 \n", - "244 False NaN NaN 270.0 \n", - "245 False NaN NaN 271.0 \n", - "246 False NaN NaN 272.0 \n", - "247 False NaN NaN 273.0 \n", - "248 False NaN NaN 274.0 \n", - "249 False NaN NaN 275.0 \n", - "250 False NaN NaN 276.0 \n", - "251 False NaN NaN 286.0 \n", - "252 False NaN NaN 287.0 \n", - "253 False NaN NaN 288.0 \n", - "254 False NaN NaN 290.0 \n", - "255 False NaN NaN 291.0 \n", - "256 False NaN NaN 292.0 \n", - "257 False NaN NaN 293.0 \n", - "258 False NaN NaN 294.0 \n", - "259 False NaN NaN 295.0 \n", - "260 False NaN NaN 296.0 \n", - "261 False NaN NaN 298.0 \n", - "262 False NaN NaN 299.0 \n", - "263 False NaN NaN 300.0 \n", - "264 False NaN NaN 301.0 \n", - "265 False NaN NaN 302.0 \n", - "266 False NaN NaN 303.0 \n", - "267 False NaN NaN 305.0 \n", - "268 False NaN NaN 306.0 \n", - "269 False NaN NaN 308.0 \n", - "270 False NaN NaN 309.0 \n", - "271 False NaN NaN 310.0 \n", - "272 False NaN NaN 311.0 \n", - "273 False NaN NaN 312.0 \n", - "274 False NaN NaN 313.0 \n", - "275 False NaN NaN 314.0 \n", - "276 False NaN NaN 316.0 \n", - "277 False NaN NaN 317.0 \n", - "278 False NaN NaN 319.0 \n", - "279 False NaN NaN 320.0 \n", - "280 False NaN NaN 322.0 \n", - "281 False NaN NaN 324.0 \n", - "282 False NaN NaN 326.0 \n", - "283 False NaN NaN 327.0 \n", - "284 False NaN NaN 328.0 \n", - "285 False NaN NaN 329.0 \n", - "286 False NaN NaN 330.0 \n", - "287 False NaN NaN 331.0 \n", - "288 False NaN NaN 332.0 \n", - "289 False NaN NaN 333.0 \n", - "290 False NaN NaN 334.0 \n", - "291 False NaN NaN 335.0 \n", - "292 False NaN NaN 337.0 \n", - "293 False NaN NaN 340.0 \n", - "294 False NaN NaN 341.0 \n", - "295 False NaN NaN 342.0 \n", - "296 False NaN NaN 343.0 \n", - "297 False NaN NaN 344.0 \n", - "298 False NaN NaN 346.0 \n", - "299 False NaN NaN 347.0 \n", - "300 False NaN NaN 348.0 \n", - "301 False NaN NaN 349.0 \n", - "302 False NaN NaN 350.0 \n", - "303 False NaN NaN 351.0 \n", - "304 False NaN NaN 352.0 \n", - "305 False NaN NaN 353.0 \n", - "306 False NaN NaN 354.0 \n", - "307 False NaN NaN 356.0 \n", - "308 False NaN NaN 357.0 \n", - "309 False NaN NaN 358.0 \n", - "310 False NaN NaN 359.0 \n", - "311 False NaN NaN 360.0 \n", - "312 False NaN NaN 361.0 \n", - "313 False NaN NaN 362.0 \n", - "314 False NaN NaN 363.0 \n", - "315 False NaN NaN 364.0 \n", - "316 False NaN NaN 366.0 \n", - "317 False NaN NaN 369.0 \n", - "318 False NaN NaN 370.0 \n", - "319 False NaN NaN 371.0 \n", - "320 False NaN NaN 374.0 \n", - "321 False NaN NaN 376.0 \n", - "322 False NaN NaN 377.0 \n", - "323 False NaN NaN 378.0 \n", - "324 False NaN NaN 379.0 \n", - "325 False NaN NaN 380.0 \n", - "326 False NaN NaN 381.0 \n", - "327 False NaN NaN 384.0 \n", - "328 False NaN NaN 387.0 \n", - "329 False NaN NaN 388.0 \n", - "330 False NaN NaN 390.0 \n", - "331 False NaN NaN 391.0 \n", - "332 False NaN NaN 392.0 \n", - "333 False NaN NaN 394.0 \n", - "334 False NaN NaN 395.0 \n", - "335 False NaN NaN 396.0 \n", - "336 False NaN NaN 397.0 \n", - "337 False NaN NaN 398.0 \n", - "338 False NaN NaN 400.0 \n", - "339 False NaN NaN 401.0 \n", - "340 False NaN NaN 402.0 \n", - "341 False NaN NaN 403.0 \n", - "342 False NaN NaN 404.0 \n", - "343 False NaN NaN 405.0 \n", - "344 False NaN NaN 406.0 \n", - "345 False NaN NaN 408.0 \n", - "346 False NaN NaN 409.0 \n", - "347 False NaN NaN 412.0 \n", - "348 False NaN NaN 414.0 \n", - "349 False NaN NaN 415.0 \n", - "350 False NaN NaN 416.0 \n", - "351 False NaN NaN 417.0 \n", - "352 False NaN NaN NaN \n", - "353 False NaN NaN NaN \n", - "354 False NaN NaN NaN \n", - "355 False NaN NaN NaN \n", - "356 False NaN NaN NaN \n", - "357 False NaN NaN NaN \n", - "358 False NaN NaN NaN \n", - "359 False NaN NaN NaN \n", - "360 False NaN NaN NaN \n", - "361 False NaN NaN NaN \n", - "362 False NaN NaN NaN \n", - "363 False NaN NaN NaN \n", - "364 False NaN NaN NaN \n", - "365 False NaN NaN NaN \n", - "366 False NaN NaN NaN \n", - "367 False NaN NaN NaN \n", - "368 False NaN NaN NaN \n", - "369 False NaN NaN NaN \n", - "370 False NaN NaN NaN \n", - "371 False NaN NaN NaN \n", - "372 False NaN NaN NaN \n", - "373 False NaN NaN NaN \n", - "\n", - " dav ontological synopsis turbo no synopsis turbo ontological synopsis \\\n", - "0 2.0 NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN 0.0 0.0 \n", - "6 1.0 NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "17 NaN NaN NaN \n", - "18 NaN NaN NaN \n", - "19 NaN NaN NaN \n", - "20 NaN NaN NaN \n", - "21 NaN NaN NaN \n", - "22 NaN NaN NaN \n", - "23 NaN NaN NaN \n", - "24 NaN NaN NaN \n", - "25 NaN NaN NaN \n", - "26 NaN NaN NaN \n", - "27 NaN NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 NaN NaN NaN \n", - "31 NaN NaN NaN \n", - "32 NaN NaN NaN \n", - "33 NaN NaN NaN \n", - "34 NaN NaN NaN \n", - "35 NaN NaN NaN \n", - "36 NaN NaN NaN \n", - "37 NaN NaN NaN \n", - "38 NaN NaN NaN \n", - "39 NaN NaN NaN \n", - "40 NaN NaN NaN \n", - "41 NaN NaN NaN \n", - "42 NaN NaN NaN \n", - "43 NaN NaN NaN \n", - "44 NaN NaN NaN \n", - "45 NaN NaN NaN \n", - "46 NaN NaN NaN \n", - "47 NaN NaN NaN \n", - "48 NaN NaN NaN \n", - "49 NaN NaN NaN \n", - "50 NaN NaN NaN \n", - "51 NaN NaN NaN \n", - "52 NaN NaN NaN \n", - "53 NaN NaN NaN \n", - "54 NaN NaN NaN \n", - "55 NaN NaN NaN \n", - "56 NaN NaN NaN \n", - "57 8.0 NaN NaN \n", - "58 NaN NaN NaN \n", - "59 NaN NaN NaN \n", - "60 NaN NaN NaN \n", - "61 NaN NaN NaN \n", - "62 NaN NaN NaN \n", - "63 NaN NaN NaN \n", - "64 NaN NaN NaN \n", - "65 NaN NaN NaN \n", - "66 NaN NaN NaN \n", - "67 NaN NaN NaN \n", - "68 NaN NaN NaN \n", - "69 NaN NaN NaN \n", - "70 NaN NaN NaN \n", - "71 NaN NaN NaN \n", - "72 7.0 NaN NaN \n", - "73 NaN NaN NaN \n", - "74 NaN NaN NaN \n", - "75 NaN NaN NaN \n", - "76 NaN NaN NaN \n", - "77 NaN NaN NaN \n", - "78 NaN NaN NaN \n", - "79 NaN NaN NaN \n", - "80 NaN NaN NaN \n", - "81 NaN NaN NaN \n", - "82 NaN NaN NaN \n", - "83 NaN NaN NaN \n", - "84 3.0 NaN NaN \n", - "85 NaN NaN NaN \n", - "86 NaN NaN NaN \n", - "87 NaN NaN NaN \n", - "88 NaN NaN NaN \n", - "89 NaN NaN NaN \n", - "90 NaN NaN NaN \n", - "91 NaN NaN NaN \n", - "92 NaN NaN NaN \n", - "93 NaN NaN NaN \n", - "94 NaN NaN NaN \n", - "95 NaN NaN NaN \n", - "96 NaN NaN NaN \n", - "97 NaN NaN NaN \n", - "98 NaN NaN NaN \n", - "99 NaN NaN NaN \n", - "100 NaN NaN NaN \n", - "101 NaN NaN NaN \n", - "102 NaN NaN NaN \n", - "103 NaN NaN NaN \n", - "104 NaN NaN NaN \n", - "105 NaN NaN NaN \n", - "106 NaN NaN NaN \n", - "107 NaN NaN NaN \n", - "108 NaN NaN NaN \n", - "109 NaN NaN NaN \n", - "110 NaN NaN NaN \n", - "111 NaN NaN NaN \n", - "112 NaN NaN NaN \n", - "113 NaN NaN NaN \n", - "114 NaN NaN NaN \n", - "115 NaN NaN NaN \n", - "116 NaN NaN NaN \n", - "117 NaN NaN NaN \n", - "118 NaN NaN NaN \n", - "119 NaN NaN NaN \n", - "120 NaN NaN NaN \n", - "121 NaN NaN NaN \n", - "122 NaN NaN NaN \n", - "123 NaN NaN NaN \n", - "124 NaN NaN NaN \n", - "125 NaN NaN NaN \n", - "126 NaN NaN NaN \n", - "127 NaN NaN NaN \n", - "128 NaN NaN NaN \n", - "129 NaN NaN NaN \n", - "130 NaN NaN NaN \n", - "131 NaN NaN NaN \n", - "132 NaN NaN NaN \n", - "133 NaN NaN NaN \n", - "134 NaN NaN NaN \n", - "135 NaN NaN NaN \n", - "136 NaN NaN NaN \n", - "137 NaN NaN NaN \n", - "138 NaN NaN NaN \n", - "139 NaN NaN NaN \n", - "140 NaN NaN NaN \n", - "141 NaN NaN NaN \n", - "142 NaN NaN NaN \n", - "143 NaN NaN NaN \n", - "144 NaN NaN NaN \n", - "145 NaN NaN NaN \n", - "146 NaN NaN NaN \n", - "147 NaN NaN NaN \n", - "148 NaN NaN NaN \n", - "149 NaN NaN NaN \n", - "150 NaN NaN NaN \n", - "151 NaN NaN NaN \n", - "152 NaN NaN NaN \n", - "153 NaN NaN NaN \n", - "154 NaN NaN NaN \n", - "155 NaN NaN NaN \n", - "156 NaN NaN NaN \n", - "157 NaN NaN NaN \n", - "158 NaN NaN NaN \n", - "159 NaN NaN NaN \n", - "160 NaN NaN NaN \n", - "161 NaN NaN NaN \n", - "162 NaN NaN NaN \n", - "163 NaN NaN NaN \n", - "164 NaN NaN NaN \n", - "165 NaN NaN NaN \n", - "166 NaN NaN NaN \n", - "167 NaN NaN NaN \n", - "168 NaN NaN NaN \n", - "169 NaN NaN NaN \n", - "170 NaN NaN NaN \n", - "171 NaN NaN NaN \n", - "172 NaN NaN NaN \n", - "173 NaN NaN NaN \n", - "174 NaN NaN NaN \n", - "175 NaN NaN NaN \n", - "176 NaN NaN NaN \n", - "177 NaN NaN NaN \n", - "178 NaN NaN NaN \n", - "179 NaN NaN NaN \n", - "180 NaN NaN NaN \n", - "181 NaN NaN NaN \n", - "182 NaN NaN NaN \n", - "183 NaN NaN NaN \n", - "184 NaN NaN NaN \n", - "185 NaN NaN NaN \n", - "186 NaN NaN NaN \n", - "187 NaN NaN NaN \n", - "188 NaN NaN NaN \n", - "189 NaN NaN NaN \n", - "190 NaN NaN NaN \n", - "191 NaN NaN NaN \n", - "192 NaN NaN NaN \n", - "193 NaN NaN NaN \n", - "194 NaN NaN NaN \n", - "195 NaN NaN NaN \n", - "196 NaN NaN NaN \n", - "197 NaN NaN NaN \n", - "198 NaN NaN NaN \n", - "199 NaN NaN NaN \n", - "200 NaN NaN NaN \n", - "201 NaN NaN NaN \n", - "202 NaN NaN NaN \n", - "203 NaN NaN NaN \n", - "204 NaN NaN NaN \n", - "205 NaN NaN NaN \n", - "206 NaN NaN NaN \n", - "207 NaN NaN NaN \n", - "208 NaN NaN NaN \n", - "209 NaN NaN NaN \n", - "210 NaN NaN NaN \n", - "211 NaN NaN NaN \n", - "212 NaN NaN NaN \n", - "213 NaN NaN NaN \n", - "214 NaN NaN NaN \n", - "215 NaN NaN NaN \n", - "216 NaN NaN NaN \n", - "217 NaN NaN NaN \n", - "218 NaN NaN NaN \n", - "219 NaN NaN NaN \n", - "220 NaN NaN NaN \n", - "221 NaN NaN NaN \n", - "222 NaN NaN NaN \n", - "223 NaN NaN NaN \n", - "224 NaN NaN NaN \n", - "225 NaN NaN NaN \n", - "226 NaN NaN NaN \n", - "227 NaN NaN NaN \n", - "228 NaN NaN NaN \n", - "229 NaN NaN NaN \n", - "230 NaN NaN NaN \n", - "231 NaN NaN NaN \n", - "232 NaN NaN NaN \n", - "233 NaN NaN NaN \n", - "234 NaN NaN NaN \n", - "235 NaN NaN NaN \n", - "236 NaN NaN NaN \n", - "237 NaN NaN NaN \n", - "238 NaN NaN NaN \n", - "239 NaN NaN NaN \n", - "240 NaN NaN NaN \n", - "241 NaN NaN NaN \n", - "242 NaN NaN NaN \n", - "243 NaN NaN NaN \n", - "244 NaN NaN NaN \n", - "245 NaN NaN NaN \n", - "246 NaN NaN NaN \n", - "247 NaN NaN NaN \n", - "248 NaN NaN NaN \n", - "249 NaN NaN NaN \n", - "250 NaN NaN NaN \n", - "251 NaN NaN NaN \n", - "252 NaN NaN NaN \n", - "253 NaN NaN NaN \n", - "254 NaN NaN NaN \n", - "255 NaN NaN NaN \n", - "256 NaN NaN NaN \n", - "257 NaN NaN NaN \n", - "258 NaN NaN NaN \n", - "259 NaN NaN NaN \n", - "260 NaN NaN NaN \n", - "261 NaN NaN NaN \n", - "262 NaN NaN NaN \n", - "263 NaN NaN NaN \n", - "264 NaN NaN NaN \n", - "265 NaN NaN NaN \n", - "266 NaN NaN NaN \n", - "267 NaN NaN NaN \n", - "268 NaN NaN NaN \n", - "269 NaN NaN NaN \n", - "270 NaN NaN NaN \n", - "271 NaN NaN NaN \n", - "272 NaN NaN NaN \n", - "273 NaN NaN NaN \n", - "274 NaN NaN NaN \n", - "275 NaN NaN NaN \n", - "276 NaN NaN NaN \n", - "277 NaN NaN NaN \n", - "278 NaN NaN NaN \n", - "279 NaN NaN NaN \n", - "280 NaN NaN NaN \n", - "281 NaN NaN NaN \n", - "282 NaN NaN NaN \n", - "283 NaN NaN NaN \n", - "284 NaN NaN NaN \n", - "285 NaN NaN NaN \n", - "286 NaN NaN NaN \n", - "287 NaN NaN NaN \n", - "288 NaN NaN NaN \n", - "289 NaN NaN NaN \n", - "290 NaN NaN NaN \n", - "291 NaN NaN NaN \n", - "292 NaN NaN NaN \n", - "293 NaN NaN NaN \n", - "294 NaN NaN NaN \n", - "295 NaN NaN NaN \n", - "296 NaN NaN NaN \n", - "297 NaN NaN NaN \n", - "298 NaN NaN NaN \n", - "299 NaN NaN NaN \n", - "300 NaN NaN NaN \n", - "301 NaN NaN NaN \n", - "302 NaN NaN NaN \n", - "303 NaN NaN NaN \n", - "304 NaN NaN NaN \n", - "305 NaN NaN NaN \n", - "306 NaN NaN NaN \n", - "307 NaN NaN NaN \n", - "308 NaN NaN NaN \n", - "309 NaN NaN NaN \n", - "310 NaN NaN NaN \n", - "311 NaN NaN NaN \n", - "312 NaN NaN NaN \n", - "313 NaN NaN NaN \n", - "314 NaN NaN NaN \n", - "315 NaN NaN NaN \n", - "316 NaN NaN NaN \n", - "317 NaN NaN NaN \n", - "318 NaN NaN NaN \n", - "319 NaN NaN NaN \n", - "320 NaN NaN NaN \n", - "321 NaN NaN NaN \n", - "322 NaN NaN NaN \n", - "323 NaN NaN NaN \n", - "324 9.0 NaN NaN \n", - "325 NaN NaN NaN \n", - "326 NaN NaN NaN \n", - "327 NaN NaN NaN \n", - "328 NaN NaN NaN \n", - "329 NaN NaN NaN \n", - "330 NaN NaN NaN \n", - "331 NaN NaN NaN \n", - "332 NaN NaN NaN \n", - "333 NaN NaN NaN \n", - "334 NaN NaN NaN \n", - "335 NaN NaN NaN \n", - "336 NaN NaN NaN \n", - "337 NaN NaN NaN \n", - "338 NaN NaN NaN \n", - "339 NaN NaN NaN \n", - "340 NaN NaN NaN \n", - "341 NaN NaN NaN \n", - "342 NaN NaN NaN \n", - "343 NaN NaN NaN \n", - "344 NaN NaN NaN \n", - "345 NaN NaN NaN \n", - "346 NaN NaN NaN \n", - "347 NaN NaN NaN \n", - "348 NaN NaN NaN \n", - "349 NaN NaN NaN \n", - "350 NaN NaN NaN \n", - "351 NaN NaN NaN \n", - "352 0.0 NaN NaN \n", - "353 4.0 NaN NaN \n", - "354 5.0 NaN NaN \n", - "355 6.0 NaN NaN \n", - "356 NaN 1.0 NaN \n", - "357 NaN 2.0 1.0 \n", - "358 NaN NaN 2.0 \n", - "359 NaN NaN 3.0 \n", - "360 NaN NaN 4.0 \n", - "361 NaN NaN NaN \n", - "362 NaN NaN NaN \n", - "363 NaN NaN NaN \n", - "364 NaN NaN NaN \n", - "365 NaN NaN NaN \n", - "366 NaN NaN NaN \n", - "367 NaN NaN NaN \n", - "368 NaN NaN NaN \n", - "369 NaN NaN NaN \n", - "370 NaN NaN NaN \n", - "371 NaN NaN NaN \n", - "372 NaN NaN NaN \n", - "373 NaN NaN NaN \n", - "\n", - " rank based dav no synopsis turbo narrative synopsis \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "17 NaN NaN NaN \n", - "18 NaN NaN NaN \n", - "19 NaN NaN NaN \n", - "20 NaN NaN NaN \n", - "21 NaN NaN NaN \n", - "22 NaN NaN NaN \n", - "23 NaN NaN NaN \n", - "24 NaN NaN NaN \n", - "25 NaN NaN NaN \n", - "26 NaN NaN NaN \n", - "27 NaN NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 NaN NaN NaN \n", - "31 NaN NaN NaN \n", - "32 NaN NaN NaN \n", - "33 NaN NaN NaN \n", - "34 NaN NaN NaN \n", - "35 NaN NaN NaN \n", - "36 NaN NaN NaN \n", - "37 NaN NaN NaN \n", - "38 NaN NaN NaN \n", - "39 NaN NaN NaN \n", - "40 NaN NaN NaN \n", - "41 NaN NaN NaN \n", - "42 NaN NaN NaN \n", - "43 NaN NaN NaN \n", - "44 NaN NaN NaN \n", - "45 NaN NaN NaN \n", - "46 NaN NaN NaN \n", - "47 NaN NaN NaN \n", - "48 NaN NaN NaN \n", - "49 NaN NaN NaN \n", - "50 NaN NaN NaN \n", - "51 NaN NaN NaN \n", - "52 NaN NaN NaN \n", - "53 NaN NaN NaN \n", - "54 NaN NaN NaN \n", - "55 NaN NaN NaN \n", - "56 NaN NaN NaN \n", - "57 NaN NaN NaN \n", - "58 NaN NaN NaN \n", - "59 NaN NaN NaN \n", - "60 NaN NaN NaN \n", - "61 NaN NaN NaN \n", - "62 NaN NaN NaN \n", - "63 NaN NaN NaN \n", - "64 NaN NaN NaN \n", - "65 NaN NaN NaN \n", - "66 NaN NaN NaN \n", - "67 NaN NaN NaN \n", - "68 NaN NaN NaN \n", - "69 0.0 NaN NaN \n", - "70 NaN NaN NaN \n", - "71 NaN NaN NaN \n", - "72 NaN NaN NaN \n", - "73 NaN NaN NaN \n", - "74 NaN NaN NaN \n", - "75 NaN NaN NaN \n", - "76 4.0 NaN NaN \n", - "77 NaN NaN NaN \n", - "78 NaN NaN NaN \n", - "79 NaN NaN NaN \n", - "80 NaN NaN NaN \n", - "81 NaN NaN NaN \n", - "82 2.0 NaN NaN \n", - "83 3.0 NaN NaN \n", - "84 NaN NaN NaN \n", - "85 NaN NaN NaN \n", - "86 NaN NaN NaN \n", - "87 NaN NaN NaN \n", - "88 NaN NaN NaN \n", - "89 NaN NaN NaN \n", - "90 NaN NaN NaN \n", - "91 NaN NaN NaN \n", - "92 NaN NaN NaN \n", - "93 NaN NaN NaN \n", - "94 NaN NaN NaN \n", - "95 NaN NaN NaN \n", - "96 NaN NaN NaN \n", - "97 NaN NaN NaN \n", - "98 NaN NaN NaN \n", - "99 NaN NaN NaN \n", - "100 NaN NaN NaN \n", - "101 NaN NaN NaN \n", - "102 NaN NaN NaN \n", - "103 NaN NaN NaN \n", - "104 NaN NaN NaN \n", - "105 NaN NaN NaN \n", - "106 NaN NaN NaN \n", - "107 7.0 NaN NaN \n", - "108 NaN NaN NaN \n", - "109 NaN NaN NaN \n", - "110 NaN NaN NaN \n", - "111 NaN NaN NaN \n", - "112 NaN NaN NaN \n", - "113 NaN NaN NaN \n", - "114 NaN NaN NaN \n", - "115 NaN NaN NaN \n", - "116 NaN 1.0 NaN \n", - "117 NaN NaN NaN \n", - "118 NaN NaN NaN \n", - "119 NaN NaN NaN \n", - "120 NaN NaN NaN \n", - "121 NaN NaN NaN \n", - "122 NaN NaN NaN \n", - "123 NaN NaN NaN \n", - "124 NaN NaN NaN \n", - "125 NaN NaN NaN \n", - "126 NaN NaN NaN \n", - "127 NaN NaN NaN \n", - "128 NaN NaN NaN \n", - "129 NaN NaN NaN \n", - "130 NaN NaN NaN \n", - "131 NaN NaN NaN \n", - "132 NaN NaN NaN \n", - "133 NaN NaN NaN \n", - "134 NaN NaN NaN \n", - "135 NaN NaN NaN \n", - "136 NaN NaN NaN \n", - "137 NaN NaN NaN \n", - "138 NaN NaN NaN \n", - "139 NaN NaN NaN \n", - "140 NaN NaN NaN \n", - "141 NaN NaN NaN \n", - "142 NaN NaN NaN \n", - "143 NaN NaN NaN \n", - "144 NaN NaN NaN \n", - "145 NaN NaN NaN \n", - "146 NaN NaN NaN \n", - "147 NaN NaN NaN \n", - "148 NaN NaN NaN \n", - "149 NaN NaN NaN \n", - "150 NaN NaN NaN \n", - "151 NaN NaN NaN \n", - "152 NaN NaN NaN \n", - "153 NaN NaN NaN \n", - "154 NaN NaN NaN \n", - "155 NaN NaN NaN \n", - "156 NaN NaN NaN \n", - "157 NaN NaN NaN \n", - "158 NaN NaN NaN \n", - "159 NaN NaN NaN \n", - "160 NaN NaN NaN \n", - "161 NaN NaN NaN \n", - "162 NaN NaN NaN \n", - "163 NaN NaN NaN \n", - "164 NaN NaN NaN \n", - "165 NaN NaN NaN \n", - "166 NaN NaN NaN \n", - "167 NaN NaN NaN \n", - "168 NaN NaN NaN \n", - "169 NaN NaN NaN \n", - "170 NaN NaN NaN \n", - "171 NaN NaN NaN \n", - "172 NaN NaN NaN \n", - "173 NaN NaN NaN \n", - "174 NaN NaN NaN \n", - "175 NaN NaN NaN \n", - "176 NaN NaN NaN \n", - "177 NaN NaN NaN \n", - "178 NaN NaN NaN \n", - "179 NaN NaN NaN \n", - "180 NaN NaN NaN \n", - "181 NaN NaN NaN \n", - "182 NaN NaN NaN \n", - "183 NaN NaN NaN \n", - "184 NaN NaN NaN \n", - "185 NaN NaN NaN \n", - "186 NaN NaN NaN \n", - "187 NaN NaN NaN \n", - "188 NaN NaN NaN \n", - "189 NaN NaN NaN \n", - "190 NaN NaN NaN \n", - "191 NaN NaN NaN \n", - "192 NaN NaN NaN \n", - "193 NaN NaN NaN \n", - "194 NaN NaN NaN \n", - "195 NaN NaN NaN \n", - "196 NaN NaN NaN \n", - "197 NaN NaN NaN \n", - "198 NaN NaN NaN \n", - "199 NaN NaN NaN \n", - "200 NaN NaN NaN \n", - "201 NaN NaN NaN \n", - "202 NaN NaN NaN \n", - "203 NaN NaN NaN \n", - "204 NaN NaN NaN \n", - "205 NaN NaN NaN \n", - "206 NaN NaN NaN \n", - "207 NaN NaN NaN \n", - "208 NaN NaN NaN \n", - "209 NaN NaN NaN \n", - "210 NaN NaN NaN \n", - "211 1.0 NaN NaN \n", - "212 NaN NaN NaN \n", - "213 NaN NaN NaN \n", - "214 NaN NaN NaN \n", - "215 NaN NaN NaN \n", - "216 NaN NaN NaN \n", - "217 NaN NaN NaN \n", - "218 NaN NaN NaN \n", - "219 NaN NaN NaN \n", - "220 NaN NaN NaN \n", - "221 NaN 3.0 NaN \n", - "222 NaN NaN NaN \n", - "223 NaN NaN NaN \n", - "224 NaN NaN NaN \n", - "225 NaN NaN NaN \n", - "226 NaN NaN NaN \n", - "227 NaN NaN NaN \n", - "228 NaN NaN NaN \n", - "229 NaN NaN NaN \n", - "230 NaN NaN NaN \n", - "231 NaN NaN NaN \n", - "232 NaN NaN NaN \n", - "233 NaN NaN NaN \n", - "234 NaN NaN NaN \n", - "235 NaN NaN NaN \n", - "236 6.0 NaN NaN \n", - "237 NaN NaN NaN \n", - "238 NaN NaN NaN \n", - "239 NaN NaN NaN \n", - "240 NaN NaN NaN \n", - "241 NaN NaN NaN \n", - "242 NaN NaN NaN \n", - "243 NaN NaN NaN \n", - "244 NaN NaN NaN \n", - "245 NaN NaN NaN \n", - "246 NaN NaN NaN \n", - "247 NaN NaN NaN \n", - "248 NaN NaN NaN \n", - "249 NaN NaN NaN \n", - "250 NaN NaN NaN \n", - "251 NaN NaN NaN \n", - "252 NaN NaN NaN \n", - "253 NaN NaN NaN \n", - "254 NaN NaN NaN \n", - "255 NaN NaN NaN \n", - "256 NaN NaN NaN \n", - "257 NaN NaN NaN \n", - "258 NaN NaN NaN \n", - "259 NaN NaN NaN \n", - "260 NaN NaN NaN \n", - "261 NaN NaN NaN \n", - "262 NaN NaN NaN \n", - "263 NaN NaN NaN \n", - "264 NaN NaN NaN \n", - "265 NaN NaN NaN \n", - "266 NaN NaN NaN \n", - "267 NaN NaN NaN \n", - "268 NaN NaN NaN \n", - "269 NaN NaN NaN \n", - "270 NaN NaN NaN \n", - "271 NaN NaN NaN \n", - "272 NaN NaN NaN \n", - "273 NaN NaN NaN \n", - "274 NaN NaN NaN \n", - "275 NaN NaN NaN \n", - "276 NaN NaN NaN \n", - "277 NaN NaN NaN \n", - "278 NaN NaN NaN \n", - "279 NaN NaN NaN \n", - "280 NaN NaN NaN \n", - "281 NaN NaN NaN \n", - "282 NaN NaN NaN \n", - "283 NaN NaN NaN \n", - "284 NaN NaN NaN \n", - "285 NaN NaN NaN \n", - "286 NaN NaN NaN \n", - "287 NaN NaN NaN \n", - "288 NaN NaN NaN \n", - "289 NaN NaN NaN \n", - "290 NaN NaN NaN \n", - "291 NaN NaN NaN \n", - "292 NaN NaN NaN \n", - "293 NaN NaN NaN \n", - "294 NaN NaN NaN \n", - "295 NaN NaN NaN \n", - "296 NaN NaN NaN \n", - "297 NaN NaN NaN \n", - "298 NaN NaN NaN \n", - "299 NaN NaN NaN \n", - "300 NaN NaN NaN \n", - "301 NaN NaN NaN \n", - "302 NaN NaN NaN \n", - "303 NaN NaN NaN \n", - "304 NaN NaN NaN \n", - "305 NaN NaN NaN \n", - "306 NaN NaN NaN \n", - "307 NaN NaN NaN \n", - "308 NaN NaN NaN \n", - "309 NaN NaN NaN \n", - "310 NaN NaN NaN \n", - "311 NaN NaN NaN \n", - "312 NaN NaN NaN \n", - "313 NaN NaN NaN \n", - "314 NaN NaN NaN \n", - "315 NaN NaN NaN \n", - "316 NaN NaN NaN \n", - "317 NaN NaN NaN \n", - "318 NaN NaN NaN \n", - "319 NaN NaN NaN \n", - "320 NaN NaN NaN \n", - "321 NaN NaN NaN \n", - "322 NaN NaN NaN \n", - "323 NaN NaN NaN \n", - "324 NaN NaN NaN \n", - "325 NaN NaN NaN \n", - "326 NaN NaN NaN \n", - "327 NaN NaN NaN \n", - "328 NaN NaN NaN \n", - "329 NaN NaN NaN \n", - "330 NaN NaN NaN \n", - "331 NaN NaN NaN \n", - "332 NaN NaN NaN \n", - "333 NaN NaN NaN \n", - "334 NaN NaN NaN \n", - "335 NaN NaN NaN \n", - "336 NaN NaN NaN \n", - "337 NaN NaN NaN \n", - "338 NaN NaN NaN \n", - "339 NaN NaN NaN \n", - "340 NaN NaN NaN \n", - "341 NaN NaN NaN \n", - "342 NaN NaN NaN \n", - "343 NaN NaN NaN \n", - "344 NaN NaN NaN \n", - "345 NaN NaN NaN \n", - "346 NaN NaN NaN \n", - "347 NaN NaN NaN \n", - "348 NaN NaN NaN \n", - "349 NaN NaN NaN \n", - "350 NaN NaN NaN \n", - "351 NaN NaN NaN \n", - "352 NaN 0.0 0.0 \n", - "353 NaN NaN NaN \n", - "354 NaN NaN NaN \n", - "355 NaN NaN NaN \n", - "356 NaN NaN NaN \n", - "357 NaN NaN NaN \n", - "358 NaN NaN NaN \n", - "359 NaN NaN NaN \n", - "360 NaN NaN NaN \n", - "361 NaN NaN 1.0 \n", - "362 NaN NaN 2.0 \n", - "363 NaN NaN 3.0 \n", - "364 NaN 2.0 NaN \n", - "365 NaN 4.0 NaN \n", - "366 NaN 5.0 NaN \n", - "367 NaN 6.0 NaN \n", - "368 NaN 7.0 NaN \n", - "369 NaN 8.0 NaN \n", - "370 NaN NaN NaN \n", - "371 NaN NaN NaN \n", - "372 NaN NaN NaN \n", - "373 5.0 NaN NaN \n", - "\n", - " dav narrative synopsis \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 NaN \n", - "8 NaN \n", - "9 NaN \n", - "10 NaN \n", - "11 NaN \n", - "12 NaN \n", - "13 NaN \n", - "14 NaN \n", - "15 NaN \n", - "16 NaN \n", - "17 NaN \n", - "18 NaN \n", - "19 NaN \n", - "20 NaN \n", - "21 NaN \n", - "22 NaN \n", - "23 NaN \n", - "24 NaN \n", - "25 NaN \n", - "26 NaN \n", - "27 NaN \n", - "28 NaN \n", - "29 NaN \n", - "30 NaN \n", - "31 NaN \n", - "32 NaN \n", - "33 NaN \n", - "34 NaN \n", - "35 NaN \n", - "36 NaN \n", - "37 NaN \n", - "38 NaN \n", - "39 NaN \n", - "40 NaN \n", - "41 NaN \n", - "42 NaN \n", - "43 NaN \n", - "44 NaN \n", - "45 NaN \n", - "46 NaN \n", - "47 NaN \n", - "48 NaN \n", - "49 NaN \n", - "50 NaN \n", - "51 NaN \n", - "52 NaN \n", - "53 NaN \n", - "54 NaN \n", - "55 NaN \n", - "56 NaN \n", - "57 NaN \n", - "58 NaN \n", - "59 NaN \n", - "60 NaN \n", - "61 NaN \n", - "62 NaN \n", - "63 NaN \n", - "64 NaN \n", - "65 NaN \n", - "66 NaN \n", - "67 NaN \n", - "68 NaN \n", - "69 NaN \n", - "70 NaN \n", - "71 NaN \n", - "72 NaN \n", - "73 NaN \n", - "74 NaN \n", - "75 NaN \n", - "76 NaN \n", - "77 NaN \n", - "78 NaN \n", - "79 NaN \n", - "80 NaN \n", - "81 NaN \n", - "82 NaN \n", - "83 NaN \n", - "84 NaN \n", - "85 NaN \n", - "86 NaN \n", - "87 NaN \n", - "88 NaN \n", - "89 NaN \n", - "90 NaN \n", - "91 NaN \n", - "92 NaN \n", - "93 NaN \n", - "94 NaN \n", - "95 NaN \n", - "96 NaN \n", - "97 NaN \n", - "98 NaN \n", - "99 NaN \n", - "100 NaN \n", - "101 NaN \n", - "102 NaN \n", - "103 NaN \n", - "104 NaN \n", - "105 NaN \n", - "106 NaN \n", - "107 NaN \n", - "108 NaN \n", - "109 NaN \n", - "110 NaN \n", - "111 NaN \n", - "112 NaN \n", - "113 NaN \n", - "114 NaN \n", - "115 NaN \n", - "116 NaN \n", - "117 NaN \n", - "118 NaN \n", - "119 NaN \n", - "120 NaN \n", - "121 NaN \n", - "122 NaN \n", - "123 NaN \n", - "124 NaN \n", - "125 NaN \n", - "126 NaN \n", - "127 NaN \n", - "128 NaN \n", - "129 NaN \n", - "130 NaN \n", - "131 NaN \n", - "132 NaN \n", - "133 NaN \n", - "134 NaN \n", - "135 NaN \n", - "136 NaN \n", - "137 NaN \n", - "138 NaN \n", - "139 NaN \n", - "140 NaN \n", - "141 NaN \n", - "142 NaN \n", - "143 NaN \n", - "144 NaN \n", - "145 NaN \n", - "146 NaN \n", - "147 NaN \n", - "148 NaN \n", - "149 NaN \n", - "150 NaN \n", - "151 NaN \n", - "152 NaN \n", - "153 NaN \n", - "154 NaN \n", - "155 NaN \n", - "156 NaN \n", - "157 NaN \n", - "158 NaN \n", - "159 NaN \n", - "160 NaN \n", - "161 NaN \n", - "162 NaN \n", - "163 NaN \n", - "164 NaN \n", - "165 NaN \n", - "166 NaN \n", - "167 NaN \n", - "168 NaN \n", - "169 NaN \n", - "170 NaN \n", - "171 NaN \n", - "172 NaN \n", - "173 NaN \n", - "174 NaN \n", - "175 NaN \n", - "176 NaN \n", - "177 NaN \n", - "178 NaN \n", - "179 NaN \n", - "180 NaN \n", - "181 NaN \n", - "182 NaN \n", - "183 NaN \n", - "184 NaN \n", - "185 NaN \n", - "186 NaN \n", - "187 NaN \n", - "188 NaN \n", - "189 NaN \n", - "190 NaN \n", - "191 NaN \n", - "192 NaN \n", - "193 NaN \n", - "194 NaN \n", - "195 NaN \n", - "196 NaN \n", - "197 NaN \n", - "198 NaN \n", - "199 NaN \n", - "200 NaN \n", - "201 NaN \n", - "202 NaN \n", - "203 NaN \n", - "204 NaN \n", - "205 NaN \n", - "206 NaN \n", - "207 NaN \n", - "208 NaN \n", - "209 NaN \n", - "210 NaN \n", - "211 NaN \n", - "212 NaN \n", - "213 NaN \n", - "214 NaN \n", - "215 NaN \n", - "216 NaN \n", - "217 NaN \n", - "218 NaN \n", - "219 NaN \n", - "220 NaN \n", - "221 NaN \n", - "222 NaN \n", - "223 NaN \n", - "224 NaN \n", - "225 NaN \n", - "226 NaN \n", - "227 NaN \n", - "228 NaN \n", - "229 NaN \n", - "230 NaN \n", - "231 NaN \n", - "232 NaN \n", - "233 NaN \n", - "234 NaN \n", - "235 NaN \n", - "236 NaN \n", - "237 NaN \n", - "238 NaN \n", - "239 NaN \n", - "240 NaN \n", - "241 NaN \n", - "242 NaN \n", - "243 NaN \n", - "244 NaN \n", - "245 NaN \n", - "246 NaN \n", - "247 NaN \n", - "248 NaN \n", - "249 NaN \n", - "250 NaN \n", - "251 NaN \n", - "252 NaN \n", - "253 NaN \n", - "254 NaN \n", - "255 NaN \n", - "256 NaN \n", - "257 NaN \n", - "258 NaN \n", - "259 NaN \n", - "260 NaN \n", - "261 NaN \n", - "262 NaN \n", - "263 NaN \n", - "264 NaN \n", - "265 NaN \n", - "266 NaN \n", - "267 NaN \n", - "268 NaN \n", - "269 NaN \n", - "270 NaN \n", - "271 NaN \n", - "272 NaN \n", - "273 NaN \n", - "274 NaN \n", - "275 NaN \n", - "276 NaN \n", - "277 NaN \n", - "278 NaN \n", - "279 NaN \n", - "280 NaN \n", - "281 NaN \n", - "282 NaN \n", - "283 NaN \n", - "284 NaN \n", - "285 NaN \n", - "286 NaN \n", - "287 NaN \n", - "288 NaN \n", - "289 NaN \n", - "290 NaN \n", - "291 NaN \n", - "292 NaN \n", - "293 NaN \n", - "294 NaN \n", - "295 NaN \n", - "296 NaN \n", - "297 NaN \n", - "298 NaN \n", - "299 NaN \n", - "300 NaN \n", - "301 NaN \n", - "302 NaN \n", - "303 NaN \n", - "304 NaN \n", - "305 NaN \n", - "306 NaN \n", - "307 NaN \n", - "308 NaN \n", - "309 NaN \n", - "310 NaN \n", - "311 NaN \n", - "312 NaN \n", - "313 NaN \n", - "314 NaN \n", - "315 NaN \n", - "316 NaN \n", - "317 NaN \n", - "318 NaN \n", - "319 NaN \n", - "320 NaN \n", - "321 NaN \n", - "322 NaN \n", - "323 NaN \n", - "324 NaN \n", - "325 NaN \n", - "326 NaN \n", - "327 NaN \n", - "328 NaN \n", - "329 NaN \n", - "330 NaN \n", - "331 NaN \n", - "332 NaN \n", - "333 NaN \n", - "334 NaN \n", - "335 NaN \n", - "336 NaN \n", - "337 NaN \n", - "338 NaN \n", - "339 NaN \n", - "340 NaN \n", - "341 NaN \n", - "342 NaN \n", - "343 NaN \n", - "344 NaN \n", - "345 NaN \n", - "346 NaN \n", - "347 NaN \n", - "348 NaN \n", - "349 NaN \n", - "350 NaN \n", - "351 NaN \n", - "352 NaN \n", - "353 NaN \n", - "354 NaN \n", - "355 NaN \n", - "356 NaN \n", - "357 NaN \n", - "358 NaN \n", - "359 0.0 \n", - "360 NaN \n", - "361 NaN \n", - "362 NaN \n", - "363 NaN \n", - "364 NaN \n", - "365 NaN \n", - "366 NaN \n", - "367 NaN \n", - "368 NaN \n", - "369 NaN \n", - "370 1.0 \n", - "371 2.0 \n", - "372 3.0 \n", - "373 NaN " - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# terms_summary(peroxisome).style.highlight_min(axis=1, props='font-weight:bold', numeric_only=True)\n", - "terms_summary(peroxisome)" - ] - }, - { - "cell_type": "markdown", - "id": "ccb68aa7", - "metadata": {}, - "source": [ - "## Sensory Ataxia" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "8562caa4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
modelmethodhas top hitin top 5in top 10size overlapsimilaritynum termsnum GO termsnr size overlapnr similaritymean p valuemin p valuemax p valueproportion significantnum novel
534N/AstandardTrueTrueTrue91.00e+009931.000.011.95e-050.041.000
535N/Astandard_no_ontologyFalseFalseFalse33.00e-014410.200.263.13e-041.000.750
528gpt-3.5-turbono_synopsisFalseFalseFalse17.69e-026500.000.802.24e-021.000.200
529gpt-3.5-turboontological_synopsisTrueTrueTrue17.69e-026510.140.801.95e-051.000.200
538N/ANoneTrueFalseFalse98.51e-031360131400.000.991.95e-051.000.010
530gpt-3.5-turbonarrative_synopsisFalseFalseFalse00.00e+003100.001.001.00e+001.000.000
531text-davinci-003no_synopsisFalseFalseFalse00.00e+007500.001.001.00e+001.000.001
532text-davinci-003ontological_synopsisFalseFalseFalse00.00e+005400.001.001.00e+001.000.001
533text-davinci-003narrative_synopsisFalseFalseFalse00.00e+008100.001.001.00e+001.000.000
536N/ArandomFalseFalseFalse00.00e+00151500.001.001.00e+001.000.009
537N/Arank_basedFalseFalseFalse00.00e+00151500.001.001.00e+001.000.000
\n", - "
" - ], - "text/plain": [ - " model method has top hit in top 5 in top 10 \\\n", - "534 N/A standard True True True \n", - "535 N/A standard_no_ontology False False False \n", - "528 gpt-3.5-turbo no_synopsis False False False \n", - "529 gpt-3.5-turbo ontological_synopsis True True True \n", - "538 N/A None True False False \n", - "530 gpt-3.5-turbo narrative_synopsis False False False \n", - "531 text-davinci-003 no_synopsis False False False \n", - "532 text-davinci-003 ontological_synopsis False False False \n", - "533 text-davinci-003 narrative_synopsis False False False \n", - "536 N/A random False False False \n", - "537 N/A rank_based False False False \n", - "\n", - " size overlap similarity num terms num GO terms nr size overlap \\\n", - "534 9 1.00e+00 9 9 3 \n", - "535 3 3.00e-01 4 4 1 \n", - "528 1 7.69e-02 6 5 0 \n", - "529 1 7.69e-02 6 5 1 \n", - "538 9 8.51e-03 1360 1314 0 \n", - "530 0 0.00e+00 3 1 0 \n", - "531 0 0.00e+00 7 5 0 \n", - "532 0 0.00e+00 5 4 0 \n", - "533 0 0.00e+00 8 1 0 \n", - "536 0 0.00e+00 15 15 0 \n", - "537 0 0.00e+00 15 15 0 \n", - "\n", - " nr similarity mean p value min p value max p value \\\n", - "534 1.00 0.01 1.95e-05 0.04 \n", - "535 0.20 0.26 3.13e-04 1.00 \n", - "528 0.00 0.80 2.24e-02 1.00 \n", - "529 0.14 0.80 1.95e-05 1.00 \n", - "538 0.00 0.99 1.95e-05 1.00 \n", - "530 0.00 1.00 1.00e+00 1.00 \n", - "531 0.00 1.00 1.00e+00 1.00 \n", - "532 0.00 1.00 1.00e+00 1.00 \n", - "533 0.00 1.00 1.00e+00 1.00 \n", - "536 0.00 1.00 1.00e+00 1.00 \n", - "537 0.00 1.00 1.00e+00 1.00 \n", - "\n", - " proportion significant num novel \n", - "534 1.00 0 \n", - "535 0.75 0 \n", - "528 0.20 0 \n", - "529 0.20 0 \n", - "538 0.01 0 \n", - "530 0.00 0 \n", - "531 0.00 1 \n", - "532 0.00 1 \n", - "533 0.00 0 \n", - "536 0.00 9 \n", - "537 0.00 0 " - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ataxia = df.query(f\"{GENESET} == 'sensory ataxia-0'\").sort_values(\"similarity\", ascending=False)\n", - "ataxia[[MODEL, METHOD] + eval_summary_cols] " - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "a744a21e", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_colwidth', None)\n", - "pd.set_option('display.max_rows', None)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "884f460d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
modelmethodgo term idsnovel labels
534N/Astandard[GO:0042552, GO:0008366, GO:0007272, GO:0007422, GO:0014037, GO:0010001, GO:0032287, GO:0006264, GO:0042063][]
535N/Astandard_no_ontology[GO:0021680, GO:0032287, GO:0006264, GO:0007422][]
528gpt-3.5-turbono_synopsis[GO:0032287, GO:0006606, GO:0015031, GO:0007599, GO:0006826][]
529gpt-3.5-turboontological_synopsis[GO:0042552, GO:0007600, GO:0016567, GO:0006260, GO:0015886][]
538N/ANone[GO:0021665, GO:0035284, GO:0014040, GO:0071837, GO:0006607, GO:0060170, GO:0005791, GO:0070417, GO:0008033, GO:0005739, GO:0043154, GO:0006096, GO:0005788, GO:0008139, GO:0060586, GO:0007165, GO:0051156, GO:0003677, GO:0000976, GO:0006839, GO:0075732, GO:0006268, GO:0045475, GO:0060173, GO:0006879, GO:0030200, GO:0007420, GO:0042391, GO:0050974, GO:0007028, GO:0003678, GO:0036494, GO:0043218, GO:0032991, GO:0048168, GO:0045444, GO:0001573, GO:0005790, GO:0032355, GO:0019901, GO:0098743, GO:0021612, GO:0034975, GO:0042564, GO:0048704, GO:0006986, GO:0098655, GO:0106074, GO:0043209, GO:0008381, GO:1904813, GO:0043005, GO:0003183, GO:0000981, GO:0030218, GO:0006801, GO:0034214, GO:0071333, GO:0034101, GO:0031410, GO:0099022, GO:0046686, GO:0065003, GO:0071260, GO:0032963, GO:0042982, GO:0061744, GO:0045893, GO:0034142, GO:0032287, GO:0005764, GO:0048143, GO:0002639, GO:0030154, GO:0140018, GO:0043231, GO:0007622, GO:0061629, GO:1904390, GO:0016887, GO:0001889, GO:0043524, GO:0002196, GO:0031069, GO:0005760, GO:0005634, GO:0032060, GO:0042474, GO:0006259, GO:0046718, GO:0003697, GO:0051087, GO:0034285, GO:0097577, GO:0042552, GO:0097367, GO:0014037, GO:0006419, GO:0048536, GO:0005789, ...][]
530gpt-3.5-turbonarrative_synopsis[GO:0030218][]
531text-davinci-003no_synopsis[GO:0007165, GO:0016192, GO:0015031, GO:0005643, GO:0006836][neurotransmitter transport]
532text-davinci-003ontological_synopsis[GO:0016567, GO:0015031, GO:0008380, GO:0006260][RNA splicing]
533text-davinci-003narrative_synopsis[GO:0032288][]
536N/Arandom[GO:0031410, GO:0042060, GO:0042475, GO:0050896, GO:0030175, GO:0043014, GO:0004758, GO:0030071, GO:0005694, GO:0008378, GO:0005886, GO:0006672, GO:0031623, GO:0070584, GO:0043588][wound healing, odontogenesis of dentin-containing tooth, filopodium, alpha-tubulin binding, serine C-palmitoyltransferase activity, regulation of mitotic metaphase/anaphase transition, galactosyltransferase activity, receptor internalization, mitochondrion morphogenesis]
537N/Arank_based[GO:0016020, GO:0005737, GO:0070062, GO:0005524, GO:0046872, GO:0005634, GO:0005576, GO:0042802, GO:0005829, GO:0005654, GO:0005886, GO:0005739, GO:0006357, GO:0005615, GO:0003723][]
\n", - "
" - ], - "text/plain": [ - " model method \\\n", - "534 N/A standard \n", - "535 N/A standard_no_ontology \n", - "528 gpt-3.5-turbo no_synopsis \n", - "529 gpt-3.5-turbo ontological_synopsis \n", - "538 N/A None \n", - "530 gpt-3.5-turbo narrative_synopsis \n", - "531 text-davinci-003 no_synopsis \n", - "532 text-davinci-003 ontological_synopsis \n", - "533 text-davinci-003 narrative_synopsis \n", - "536 N/A random \n", - "537 N/A rank_based \n", - "\n", - " go term ids \\\n", - "534 [GO:0042552, GO:0008366, GO:0007272, GO:0007422, GO:0014037, GO:0010001, GO:0032287, GO:0006264, GO:0042063] \n", - "535 [GO:0021680, GO:0032287, GO:0006264, GO:0007422] \n", - "528 [GO:0032287, GO:0006606, GO:0015031, GO:0007599, GO:0006826] \n", - "529 [GO:0042552, GO:0007600, GO:0016567, GO:0006260, GO:0015886] \n", - "538 [GO:0021665, GO:0035284, GO:0014040, GO:0071837, GO:0006607, GO:0060170, GO:0005791, GO:0070417, GO:0008033, GO:0005739, GO:0043154, GO:0006096, GO:0005788, GO:0008139, GO:0060586, GO:0007165, GO:0051156, GO:0003677, GO:0000976, GO:0006839, GO:0075732, GO:0006268, GO:0045475, GO:0060173, GO:0006879, GO:0030200, GO:0007420, GO:0042391, GO:0050974, GO:0007028, GO:0003678, GO:0036494, GO:0043218, GO:0032991, GO:0048168, GO:0045444, GO:0001573, GO:0005790, GO:0032355, GO:0019901, GO:0098743, GO:0021612, GO:0034975, GO:0042564, GO:0048704, GO:0006986, GO:0098655, GO:0106074, GO:0043209, GO:0008381, GO:1904813, GO:0043005, GO:0003183, GO:0000981, GO:0030218, GO:0006801, GO:0034214, GO:0071333, GO:0034101, GO:0031410, GO:0099022, GO:0046686, GO:0065003, GO:0071260, GO:0032963, GO:0042982, GO:0061744, GO:0045893, GO:0034142, GO:0032287, GO:0005764, GO:0048143, GO:0002639, GO:0030154, GO:0140018, GO:0043231, GO:0007622, GO:0061629, GO:1904390, GO:0016887, GO:0001889, GO:0043524, GO:0002196, GO:0031069, GO:0005760, GO:0005634, GO:0032060, GO:0042474, GO:0006259, GO:0046718, GO:0003697, GO:0051087, GO:0034285, GO:0097577, GO:0042552, GO:0097367, GO:0014037, GO:0006419, GO:0048536, GO:0005789, ...] \n", - "530 [GO:0030218] \n", - "531 [GO:0007165, GO:0016192, GO:0015031, GO:0005643, GO:0006836] \n", - "532 [GO:0016567, GO:0015031, GO:0008380, GO:0006260] \n", - "533 [GO:0032288] \n", - "536 [GO:0031410, GO:0042060, GO:0042475, GO:0050896, GO:0030175, GO:0043014, GO:0004758, GO:0030071, GO:0005694, GO:0008378, GO:0005886, GO:0006672, GO:0031623, GO:0070584, GO:0043588] \n", - "537 [GO:0016020, GO:0005737, GO:0070062, GO:0005524, GO:0046872, GO:0005634, GO:0005576, GO:0042802, GO:0005829, GO:0005654, GO:0005886, GO:0005739, GO:0006357, GO:0005615, GO:0003723] \n", - "\n", - " novel labels \n", - "534 [] \n", - "535 [] \n", - "528 [] \n", - "529 [] \n", - "538 [] \n", - "530 [] \n", - "531 [neurotransmitter transport] \n", - "532 [RNA splicing] \n", - "533 [] \n", - "536 [wound healing, odontogenesis of dentin-containing tooth, filopodium, alpha-tubulin binding, serine C-palmitoyltransferase activity, regulation of mitotic metaphase/anaphase transition, galactosyltransferase activity, receptor internalization, mitochondrion morphogenesis] \n", - "537 [] " - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ataxia[[MODEL, METHOD, GO_TERM_IDS, NOVEL_LABELS]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06560bd8", - "metadata": {}, - "outputs": [], - "source": [ - "terms_summary(ataxia)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "76c0b8ee", - "metadata": {}, - "outputs": [], - "source": [ - "def retrieve_payload(geneset, method):\n", - " for comp in comps:\n", - " if comp.name == geneset:\n", - " return comp.payloads[method]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "17c4d4f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Summary: These genes are involved in various processes related to the peripheral nervous system and nucleic acid metabolism, including myelination, DNA replication, and protein ubiquitination.\n", - "Mechanism: These genes may be involved in the maintenance, repair, and regulation of the peripheral nervous system and nucleic acids.\n", - "Enriched Terms: peripheral nervous system myelin maintenance; myelination; DNA replication; protein ubiquitination.\n" - ] - } - ], - "source": [ - "print(retrieve_payload(\"sensory ataxia-0\", \"gpt-3.5-turbo.ontological_synopsis\").response_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "2fb5f713", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Summary: Genes are involved in various neurological and metabolic disorders. Enriched terms include \"peripheral nervous system development,\" \"neurological system process,\" and \"mitochondrial DNA replication.\"\n", - "\n", - "Mechanism: The enriched terms suggest a common theme of neurological and metabolic dysfunction related to the peripheral nervous system and mitochondrial function.\n", - "\n", - "Enriched Terms: peripheral nervous system development; neurological system process; mitochondrial DNA replication.\n" - ] - } - ], - "source": [ - "print(retrieve_payload(\"sensory ataxia-0\", \"gpt-3.5-turbo.narrative_synopsis\").response_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "652ef2a2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Summary: Enriched terms are related to myelin sheath formation, neuronal development, and protein processing.\n", - "Enriched Terms: Myelination; Neuron development; Protein folding; Protein targeting to ER; ATP binding; Axon ensheathment; Mitochondrial genome maintenance; Zinc ion binding.\n" - ] - } - ], - "source": [ - "print(retrieve_payload(\"sensory ataxia-0\", \"gpt-3.5-turbo.no_synopsis\").response_text)" - ] - }, - { - "cell_type": "markdown", - "id": "5876611c", - "metadata": {}, - "source": [ - "## Endocytosis" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "4df09ade", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idlabelredundantstandardstandard no ontologyturbo gpt-3.5-turbo.ontological synopsisdav text-davinci-003.narrative synopsisdav text-davinci-003.ontological synopsisturbo gpt-3.5-turbo.narrative synopsisturbo gpt-3.5-turbo.no synopsisrank baseddav text-davinci-003.no synopsisrandom
0GO:0006907pinocytosisFalse0.01.03.0NaNNaNNaNNaNNaNNaNNaN
1GO:0006897endocytosisTrue1.06.00.01.013.00.01.0NaNNaNNaN
2GO:0044351macropinocytosisTrue2.00.0NaN5.03.0NaNNaNNaNNaNNaN
3GO:0016192vesicle-mediated transportTrue3.0NaNNaN3.0NaNNaNNaNNaNNaNNaN
4GO:0030100regulation of endocytosisFalse4.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
5GO:0006810transportTrue5.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
6GO:0051234establishment of localizationTrue6.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
7GO:0045807positive regulation of endocytosisTrue7.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
8GO:0060627regulation of vesicle-mediated transportTrue8.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
9GO:0051179localizationTrue9.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
10GO:0031410cytoplasmic vesicleFalse10.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
11GO:0097708intracellular vesicleTrue11.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
12GO:0050766positive regulation of phagocytosisTrue12.04.0NaNNaNNaNNaNNaNNaNNaNNaN
13GO:0048518positive regulation of biological processTrue13.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
14GO:0050764regulation of phagocytosisTrue14.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
15GO:0051128regulation of cellular component organizationTrue15.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
16GO:0031982vesicleTrue16.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
17GO:0150094amyloid-beta clearance by cellular catabolic processFalse17.03.0NaNNaNNaNNaNNaNNaNNaNNaN
18GO:0006909phagocytosisTrue18.017.04.0NaN10.0NaNNaNNaNNaNNaN
19GO:0051049regulation of transportTrue19.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
20GO:0006898receptor-mediated endocytosisTrue20.02.0NaNNaNNaNNaNNaNNaNNaNNaN
21GO:0051050positive regulation of transportTrue21.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
22GO:0005041low-density lipoprotein particle receptor activityTrue22.05.0NaNNaNNaNNaNNaNNaNNaNNaN
23GO:0030139endocytic vesicleTrue23.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
24GO:0030228lipoprotein particle receptor activityTrue24.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
25GO:0030666endocytic vesicle membraneTrue25.018.0NaNNaNNaNNaNNaNNaNNaNNaN
26GO:0097242amyloid-beta clearanceTrue26.022.0NaNNaNNaNNaNNaNNaNNaNNaN
27GO:0051130positive regulation of cellular component organizationTrue27.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
28GO:0060907positive regulation of macrophage cytokine productionTrue28.08.0NaNNaNNaNNaNNaNNaNNaNNaN
29GO:0032879regulation of localizationTrue29.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
30GO:0048522positive regulation of cellular processTrue30.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
31GO:0002277myeloid dendritic cell activation involved in immune responseFalse31.09.0NaNNaNNaNNaNNaNNaNNaNNaN
32GO:0030659cytoplasmic vesicle membraneTrue32.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
33GO:0012506vesicle membraneTrue33.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
34GO:0061081positive regulation of myeloid leukocyte cytokine production involved in immune responseTrue34.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
35GO:0010935regulation of macrophage cytokine productionTrue35.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
36GO:0048583regulation of response to stimulusFalse36.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
37GO:1905167positive regulation of lysosomal protein catabolic processTrue37.011.0NaNNaNNaNNaNNaNNaNNaNNaN
38GO:0009894regulation of catabolic processTrue38.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
39GO:0023051regulation of signalingFalse39.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
40GO:0051641cellular localizationTrue40.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
41GO:1904352positive regulation of protein catabolic process in the vacuoleTrue41.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
42GO:0070508cholesterol importTrue42.013.0NaNNaNNaNNaNNaNNaNNaNNaN
43GO:0031347regulation of defense responseTrue43.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
44GO:0005794Golgi apparatusFalse44.014.0NaNNaNNaNNaNNaNNaNNaNNaN
45GO:1901700response to oxygen-containing compoundFalse45.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
46GO:0061024membrane organizationFalse46.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
47GO:0009966regulation of signal transductionTrue47.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
48GO:0015031protein transportTrue48.0NaNNaN2.0NaNNaNNaNNaNNaNNaN
49GO:0048523negative regulation of cellular processFalse49.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
50GO:0032429regulation of phospholipase A2 activityFalseNaN7.0NaNNaNNaNNaNNaNNaNNaNNaN
51GO:0034381plasma lipoprotein particle clearanceFalseNaN10.0NaNNaNNaNNaNNaNNaNNaNNaN
52GO:0031623receptor internalizationFalseNaN12.0NaNNaNNaNNaNNaNNaNNaNNaN
53GO:0009931calcium-dependent protein serine/threonine kinase activityFalseNaN15.0NaNNaNNaNNaNNaNNaNNaNNaN
54GO:0005905clathrin-coated pitFalseNaN16.0NaNNaNNaNNaNNaNNaNNaNNaN
55GO:0032050clathrin heavy chain bindingFalseNaN19.0NaNNaNNaNNaNNaNNaNNaNNaN
56GO:0030299intestinal cholesterol absorptionFalseNaN20.0NaNNaNNaNNaNNaNNaNNaNNaN
57GO:0001540amyloid-beta bindingFalseNaN21.0NaNNaNNaNNaNNaNNaNNaNNaN
58GO:0034383low-density lipoprotein particle clearanceFalseNaN23.0NaNNaNNaNNaNNaNNaNNaNNaN
59GO:0032760positive regulation of tumor necrosis factor productionFalseNaN24.0NaNNaNNaNNaNNaNNaNNaNNaN
60GO:0042953lipoprotein transportFalseNaN25.0NaNNaNNaNNaNNaNNaNNaNNaN
61GO:0071404cellular response to low-density lipoprotein particle stimulusFalseNaN26.0NaNNaNNaNNaNNaNNaNNaNNaN
62GO:0030169low-density lipoprotein particle bindingFalseNaN27.0NaNNaNNaNNaNNaNNaNNaNNaN
63actin filament formationNoneFalseNaNNaNNaN0.0NaNNaNNaNNaNNaNNaN
64lipid and fatty acid metabolismNoneFalseNaNNaNNaN4.0NaNNaNNaNNaNNaNNaN
65GO:0007155cell adhesionFalseNaNNaNNaN6.0NaNNaNNaNNaNNaNNaN
66intracellular traffickingNoneFalseNaNNaN1.0NaNNaN2.0NaNNaNNaNNaN
67GO:0005480obsolete vesicle transportFalseNaNNaN2.0NaNNaNNaNNaNNaNNaNNaN
68GO:0055085transmembrane transportFalseNaNNaN5.0NaNNaNNaNNaNNaNNaNNaN
69GO:0006893Golgi to plasma membrane transportFalseNaNNaN6.0NaNNaNNaNNaNNaNNaNNaN
70protein binding activityNoneFalseNaNNaNNaNNaN0.0NaNNaNNaNNaNNaN
71GO:0005886plasma membraneFalseNaNNaNNaNNaN1.0NaNNaN11.0NaNNaN
72small gtpase binding activityNoneFalseNaNNaNNaNNaN2.0NaNNaNNaNNaNNaN
73GO:0043001Golgi to plasma membrane protein transportFalseNaNNaNNaNNaN4.0NaNNaNNaNNaNNaN
74tyrosine kinase activityNoneFalseNaNNaNNaNNaN5.0NaNNaNNaNNaNNaN
75GO:0009986cell surfaceFalseNaNNaNNaNNaN6.0NaNNaNNaNNaNNaN
76cytoskeletal protein binding activityNoneFalseNaNNaNNaNNaN7.0NaNNaNNaNNaNNaN
77GO:0005783endoplasmic reticulumFalseNaNNaNNaNNaN8.0NaNNaNNaNNaNNaN
78GO:0032456endocytic recyclingFalseNaNNaNNaNNaN9.0NaNNaNNaNNaNNaN
79GO:0051260protein homooligomerizationFalseNaNNaNNaNNaN11.0NaNNaNNaNNaNNaN
80GO:0097320plasma membrane tubulationFalseNaNNaNNaNNaN12.0NaNNaNNaNNaNNaN
81GO:0010468regulation of gene expressionFalseNaNNaNNaNNaN14.0NaNNaNNaNNaNNaN
82GO:0048010vascular endothelial growth factor receptor signaling pathwayFalseNaNNaNNaNNaN15.0NaNNaNNaNNaNNaN
83GO:0007010cytoskeleton organizationFalseNaNNaNNaNNaNNaN1.0NaNNaNNaNNaN
84GO:0016197endosomal transportFalseNaNNaNNaNNaNNaNNaN0.0NaNNaNNaN
85GO:0007041lysosomal transportFalseNaNNaNNaNNaNNaNNaN2.0NaNNaNNaN
86g-protein signalling pathwayNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN
87mapk signalling pathwayNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN1.0NaN
88GO:0006631fatty acid metabolic processFalseNaNNaNNaNNaNNaNNaNNaNNaN2.0NaN
89calcium signalling pathwayNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN3.0NaN
90cell membrane composition and organizationNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN4.0NaN
91protein kinase signallingNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN5.0NaN
92apoptosis regulationNoneFalseNaNNaNNaNNaNNaNNaNNaNNaN6.0NaN
93GO:0140105interleukin-10-mediated signaling pathwayFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
94GO:0016363nuclear matrixFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0
95GO:0016579protein deubiquitinationFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN2.0
96GO:0007626locomotory behaviorFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN3.0
97GO:0008427calcium-dependent protein kinase inhibitor activityFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN4.0
98GO:0050255ribitol 2-dehydrogenase activityFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN5.0
99GO:0000228nuclear chromosomeFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN6.0
100GO:0001917photoreceptor inner segmentFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN7.0
101GO:0007165signal transductionFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN8.0
102GO:0035578azurophil granule lumenFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN9.0
103GO:0000976transcription cis-regulatory region bindingFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN10.0
104GO:0005576extracellular regionFalseNaNNaNNaNNaNNaNNaNNaN13.0NaN11.0
105GO:0009887animal organ morphogenesisFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN12.0
106GO:0043085positive regulation of catalytic activityFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN13.0
107GO:0042742defense response to bacteriumFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN14.0
108GO:0098655monoatomic cation transmembrane transportFalseNaNNaNNaNNaNNaNNaNNaNNaNNaN15.0
109GO:0006357regulation of transcription by RNA polymerase IIFalseNaNNaNNaNNaNNaNNaNNaN0.0NaNNaN
110GO:0046872metal ion bindingFalseNaNNaNNaNNaNNaNNaNNaN1.0NaNNaN
111GO:0016020membraneFalseNaNNaNNaNNaNNaNNaNNaN2.0NaNNaN
112GO:0005739mitochondrionFalseNaNNaNNaNNaNNaNNaNNaN3.0NaNNaN
113GO:0003723RNA bindingFalseNaNNaNNaNNaNNaNNaNNaN4.0NaNNaN
114GO:0005615extracellular spaceFalseNaNNaNNaNNaNNaNNaNNaN5.0NaNNaN
115GO:0005829cytosolFalseNaNNaNNaNNaNNaNNaNNaN6.0NaNNaN
116GO:0042802identical protein bindingFalseNaNNaNNaNNaNNaNNaNNaN7.0NaNNaN
117GO:0045944positive regulation of transcription by RNA polymerase IIFalseNaNNaNNaNNaNNaNNaNNaN8.0NaNNaN
118GO:0005524ATP bindingFalseNaNNaNNaNNaNNaNNaNNaN9.0NaNNaN
119GO:0070062extracellular exosomeFalseNaNNaNNaNNaNNaNNaNNaN10.0NaNNaN
120GO:0005737cytoplasmFalseNaNNaNNaNNaNNaNNaNNaN12.0NaNNaN
121GO:0005634nucleusFalseNaNNaNNaNNaNNaNNaNNaN14.0NaNNaN
122GO:0005654nucleoplasmFalseNaNNaNNaNNaNNaNNaNNaN15.0NaNNaN
\n", - "
" - ], - "text/plain": [ - " id \\\n", - "0 GO:0006907 \n", - "1 GO:0006897 \n", - "2 GO:0044351 \n", - "3 GO:0016192 \n", - "4 GO:0030100 \n", - "5 GO:0006810 \n", - "6 GO:0051234 \n", - "7 GO:0045807 \n", - "8 GO:0060627 \n", - "9 GO:0051179 \n", - "10 GO:0031410 \n", - "11 GO:0097708 \n", - "12 GO:0050766 \n", - "13 GO:0048518 \n", - "14 GO:0050764 \n", - "15 GO:0051128 \n", - "16 GO:0031982 \n", - "17 GO:0150094 \n", - "18 GO:0006909 \n", - "19 GO:0051049 \n", - "20 GO:0006898 \n", - "21 GO:0051050 \n", - "22 GO:0005041 \n", - "23 GO:0030139 \n", - "24 GO:0030228 \n", - "25 GO:0030666 \n", - "26 GO:0097242 \n", - "27 GO:0051130 \n", - "28 GO:0060907 \n", - "29 GO:0032879 \n", - "30 GO:0048522 \n", - "31 GO:0002277 \n", - "32 GO:0030659 \n", - "33 GO:0012506 \n", - "34 GO:0061081 \n", - "35 GO:0010935 \n", - "36 GO:0048583 \n", - "37 GO:1905167 \n", - "38 GO:0009894 \n", - "39 GO:0023051 \n", - "40 GO:0051641 \n", - "41 GO:1904352 \n", - "42 GO:0070508 \n", - "43 GO:0031347 \n", - "44 GO:0005794 \n", - "45 GO:1901700 \n", - "46 GO:0061024 \n", - "47 GO:0009966 \n", - "48 GO:0015031 \n", - "49 GO:0048523 \n", - "50 GO:0032429 \n", - "51 GO:0034381 \n", - "52 GO:0031623 \n", - "53 GO:0009931 \n", - "54 GO:0005905 \n", - "55 GO:0032050 \n", - "56 GO:0030299 \n", - "57 GO:0001540 \n", - "58 GO:0034383 \n", - "59 GO:0032760 \n", - "60 GO:0042953 \n", - "61 GO:0071404 \n", - "62 GO:0030169 \n", - "63 actin filament formation \n", - "64 lipid and fatty acid metabolism \n", - "65 GO:0007155 \n", - "66 intracellular trafficking \n", - "67 GO:0005480 \n", - "68 GO:0055085 \n", - "69 GO:0006893 \n", - "70 protein binding activity \n", - "71 GO:0005886 \n", - "72 small gtpase binding activity \n", - "73 GO:0043001 \n", - "74 tyrosine kinase activity \n", - "75 GO:0009986 \n", - "76 cytoskeletal protein binding activity \n", - "77 GO:0005783 \n", - "78 GO:0032456 \n", - "79 GO:0051260 \n", - "80 GO:0097320 \n", - "81 GO:0010468 \n", - "82 GO:0048010 \n", - "83 GO:0007010 \n", - "84 GO:0016197 \n", - "85 GO:0007041 \n", - "86 g-protein signalling pathway \n", - "87 mapk signalling pathway \n", - "88 GO:0006631 \n", - "89 calcium signalling pathway \n", - "90 cell membrane composition and organization \n", - "91 protein kinase signalling \n", - "92 apoptosis regulation \n", - "93 GO:0140105 \n", - "94 GO:0016363 \n", - "95 GO:0016579 \n", - "96 GO:0007626 \n", - "97 GO:0008427 \n", - "98 GO:0050255 \n", - "99 GO:0000228 \n", - "100 GO:0001917 \n", - "101 GO:0007165 \n", - "102 GO:0035578 \n", - "103 GO:0000976 \n", - "104 GO:0005576 \n", - "105 GO:0009887 \n", - "106 GO:0043085 \n", - "107 GO:0042742 \n", - "108 GO:0098655 \n", - "109 GO:0006357 \n", - "110 GO:0046872 \n", - "111 GO:0016020 \n", - "112 GO:0005739 \n", - "113 GO:0003723 \n", - "114 GO:0005615 \n", - "115 GO:0005829 \n", - "116 GO:0042802 \n", - "117 GO:0045944 \n", - "118 GO:0005524 \n", - "119 GO:0070062 \n", - "120 GO:0005737 \n", - "121 GO:0005634 \n", - "122 GO:0005654 \n", - "\n", - " label \\\n", - "0 pinocytosis \n", - "1 endocytosis \n", - "2 macropinocytosis \n", - "3 vesicle-mediated transport \n", - "4 regulation of endocytosis \n", - "5 transport \n", - "6 establishment of localization \n", - "7 positive regulation of endocytosis \n", - "8 regulation of vesicle-mediated transport \n", - "9 localization \n", - "10 cytoplasmic vesicle \n", - "11 intracellular vesicle \n", - "12 positive regulation of phagocytosis \n", - "13 positive regulation of biological process \n", - "14 regulation of phagocytosis \n", - "15 regulation of cellular component organization \n", - "16 vesicle \n", - "17 amyloid-beta clearance by cellular catabolic process \n", - "18 phagocytosis \n", - "19 regulation of transport \n", - "20 receptor-mediated endocytosis \n", - "21 positive regulation of transport \n", - "22 low-density lipoprotein particle receptor activity \n", - "23 endocytic vesicle \n", - "24 lipoprotein particle receptor activity \n", - "25 endocytic vesicle membrane \n", - "26 amyloid-beta clearance \n", - "27 positive regulation of cellular component organization \n", - "28 positive regulation of macrophage cytokine production \n", - "29 regulation of localization \n", - "30 positive regulation of cellular process \n", - "31 myeloid dendritic cell activation involved in immune response \n", - "32 cytoplasmic vesicle membrane \n", - "33 vesicle membrane \n", - "34 positive regulation of myeloid leukocyte cytokine production involved in immune response \n", - "35 regulation of macrophage cytokine production \n", - "36 regulation of response to stimulus \n", - "37 positive regulation of lysosomal protein catabolic process \n", - "38 regulation of catabolic process \n", - "39 regulation of signaling \n", - "40 cellular localization \n", - "41 positive regulation of protein catabolic process in the vacuole \n", - "42 cholesterol import \n", - "43 regulation of defense response \n", - "44 Golgi apparatus \n", - "45 response to oxygen-containing compound \n", - "46 membrane organization \n", - "47 regulation of signal transduction \n", - "48 protein transport \n", - "49 negative regulation of cellular process \n", - "50 regulation of phospholipase A2 activity \n", - "51 plasma lipoprotein particle clearance \n", - "52 receptor internalization \n", - "53 calcium-dependent protein serine/threonine kinase activity \n", - "54 clathrin-coated pit \n", - "55 clathrin heavy chain binding \n", - "56 intestinal cholesterol absorption \n", - "57 amyloid-beta binding \n", - "58 low-density lipoprotein particle clearance \n", - "59 positive regulation of tumor necrosis factor production \n", - "60 lipoprotein transport \n", - "61 cellular response to low-density lipoprotein particle stimulus \n", - "62 low-density lipoprotein particle binding \n", - "63 None \n", - "64 None \n", - "65 cell adhesion \n", - "66 None \n", - "67 obsolete vesicle transport \n", - "68 transmembrane transport \n", - "69 Golgi to plasma membrane transport \n", - "70 None \n", - "71 plasma membrane \n", - "72 None \n", - "73 Golgi to plasma membrane protein transport \n", - "74 None \n", - "75 cell surface \n", - "76 None \n", - "77 endoplasmic reticulum \n", - "78 endocytic recycling \n", - "79 protein homooligomerization \n", - "80 plasma membrane tubulation \n", - "81 regulation of gene expression \n", - "82 vascular endothelial growth factor receptor signaling pathway \n", - "83 cytoskeleton organization \n", - "84 endosomal transport \n", - "85 lysosomal transport \n", - "86 None \n", - "87 None \n", - "88 fatty acid metabolic process \n", - "89 None \n", - "90 None \n", - "91 None \n", - "92 None \n", - "93 interleukin-10-mediated signaling pathway \n", - "94 nuclear matrix \n", - "95 protein deubiquitination \n", - "96 locomotory behavior \n", - "97 calcium-dependent protein kinase inhibitor activity \n", - "98 ribitol 2-dehydrogenase activity \n", - "99 nuclear chromosome \n", - "100 photoreceptor inner segment \n", - "101 signal transduction \n", - "102 azurophil granule lumen \n", - "103 transcription cis-regulatory region binding \n", - "104 extracellular region \n", - "105 animal organ morphogenesis \n", - "106 positive regulation of catalytic activity \n", - "107 defense response to bacterium \n", - "108 monoatomic cation transmembrane transport \n", - "109 regulation of transcription by RNA polymerase II \n", - "110 metal ion binding \n", - "111 membrane \n", - "112 mitochondrion \n", - "113 RNA binding \n", - "114 extracellular space \n", - "115 cytosol \n", - "116 identical protein binding \n", - "117 positive regulation of transcription by RNA polymerase II \n", - "118 ATP binding \n", - "119 extracellular exosome \n", - "120 cytoplasm \n", - "121 nucleus \n", - "122 nucleoplasm \n", - "\n", - " redundant standard standard no ontology \\\n", - "0 False 0.0 1.0 \n", - "1 True 1.0 6.0 \n", - "2 True 2.0 0.0 \n", - "3 True 3.0 NaN \n", - "4 False 4.0 NaN \n", - "5 True 5.0 NaN \n", - "6 True 6.0 NaN \n", - "7 True 7.0 NaN \n", - "8 True 8.0 NaN \n", - "9 True 9.0 NaN \n", - "10 False 10.0 NaN \n", - "11 True 11.0 NaN \n", - "12 True 12.0 4.0 \n", - "13 True 13.0 NaN \n", - "14 True 14.0 NaN \n", - "15 True 15.0 NaN \n", - "16 True 16.0 NaN \n", - "17 False 17.0 3.0 \n", - "18 True 18.0 17.0 \n", - "19 True 19.0 NaN \n", - "20 True 20.0 2.0 \n", - "21 True 21.0 NaN \n", - "22 True 22.0 5.0 \n", - "23 True 23.0 NaN \n", - "24 True 24.0 NaN \n", - "25 True 25.0 18.0 \n", - "26 True 26.0 22.0 \n", - "27 True 27.0 NaN \n", - "28 True 28.0 8.0 \n", - "29 True 29.0 NaN \n", - "30 True 30.0 NaN \n", - "31 False 31.0 9.0 \n", - "32 True 32.0 NaN \n", - "33 True 33.0 NaN \n", - "34 True 34.0 NaN \n", - "35 True 35.0 NaN \n", - "36 False 36.0 NaN \n", - "37 True 37.0 11.0 \n", - "38 True 38.0 NaN \n", - "39 False 39.0 NaN \n", - "40 True 40.0 NaN \n", - "41 True 41.0 NaN \n", - "42 True 42.0 13.0 \n", - "43 True 43.0 NaN \n", - "44 False 44.0 14.0 \n", - "45 False 45.0 NaN \n", - "46 False 46.0 NaN \n", - "47 True 47.0 NaN \n", - "48 True 48.0 NaN \n", - "49 False 49.0 NaN \n", - "50 False NaN 7.0 \n", - "51 False NaN 10.0 \n", - "52 False NaN 12.0 \n", - "53 False NaN 15.0 \n", - "54 False NaN 16.0 \n", - "55 False NaN 19.0 \n", - "56 False NaN 20.0 \n", - "57 False NaN 21.0 \n", - "58 False NaN 23.0 \n", - "59 False NaN 24.0 \n", - "60 False NaN 25.0 \n", - "61 False NaN 26.0 \n", - "62 False NaN 27.0 \n", - "63 False NaN NaN \n", - "64 False NaN NaN \n", - "65 False NaN NaN \n", - "66 False NaN NaN \n", - "67 False NaN NaN \n", - "68 False NaN NaN \n", - "69 False NaN NaN \n", - "70 False NaN NaN \n", - "71 False NaN NaN \n", - "72 False NaN NaN \n", - "73 False NaN NaN \n", - "74 False NaN NaN \n", - "75 False NaN NaN \n", - "76 False NaN NaN \n", - "77 False NaN NaN \n", - "78 False NaN NaN \n", - "79 False NaN NaN \n", - "80 False NaN NaN \n", - "81 False NaN NaN \n", - "82 False NaN NaN \n", - "83 False NaN NaN \n", - "84 False NaN NaN \n", - "85 False NaN NaN \n", - "86 False NaN NaN \n", - "87 False NaN NaN \n", - "88 False NaN NaN \n", - "89 False NaN NaN \n", - "90 False NaN NaN \n", - "91 False NaN NaN \n", - "92 False NaN NaN \n", - "93 False NaN NaN \n", - "94 False NaN NaN \n", - "95 False NaN NaN \n", - "96 False NaN NaN \n", - "97 False NaN NaN \n", - "98 False NaN NaN \n", - "99 False NaN NaN \n", - "100 False NaN NaN \n", - "101 False NaN NaN \n", - "102 False NaN NaN \n", - "103 False NaN NaN \n", - "104 False NaN NaN \n", - "105 False NaN NaN \n", - "106 False NaN NaN \n", - "107 False NaN NaN \n", - "108 False NaN NaN \n", - "109 False NaN NaN \n", - "110 False NaN NaN \n", - "111 False NaN NaN \n", - "112 False NaN NaN \n", - "113 False NaN NaN \n", - "114 False NaN NaN \n", - "115 False NaN NaN \n", - "116 False NaN NaN \n", - "117 False NaN NaN \n", - "118 False NaN NaN \n", - "119 False NaN NaN \n", - "120 False NaN NaN \n", - "121 False NaN NaN \n", - "122 False NaN NaN \n", - "\n", - " turbo gpt-3.5-turbo.ontological synopsis \\\n", - "0 3.0 \n", - "1 0.0 \n", - "2 NaN \n", - "3 NaN \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 NaN \n", - "8 NaN \n", - "9 NaN \n", - "10 NaN \n", - "11 NaN \n", - "12 NaN \n", - "13 NaN \n", - "14 NaN \n", - "15 NaN \n", - "16 NaN \n", - "17 NaN \n", - "18 4.0 \n", - "19 NaN \n", - "20 NaN \n", - "21 NaN \n", - "22 NaN \n", - "23 NaN \n", - "24 NaN \n", - "25 NaN \n", - "26 NaN \n", - "27 NaN \n", - "28 NaN \n", - "29 NaN \n", - "30 NaN \n", - "31 NaN \n", - "32 NaN \n", - "33 NaN \n", - "34 NaN \n", - "35 NaN \n", - "36 NaN \n", - "37 NaN \n", - "38 NaN \n", - "39 NaN \n", - "40 NaN \n", - "41 NaN \n", - "42 NaN \n", - "43 NaN \n", - "44 NaN \n", - "45 NaN \n", - "46 NaN \n", - "47 NaN \n", - "48 NaN \n", - "49 NaN \n", - "50 NaN \n", - "51 NaN \n", - "52 NaN \n", - "53 NaN \n", - "54 NaN \n", - "55 NaN \n", - "56 NaN \n", - "57 NaN \n", - "58 NaN \n", - "59 NaN \n", - "60 NaN \n", - "61 NaN \n", - "62 NaN \n", - "63 NaN \n", - "64 NaN \n", - "65 NaN \n", - "66 1.0 \n", - "67 2.0 \n", - "68 5.0 \n", - "69 6.0 \n", - "70 NaN \n", - "71 NaN \n", - "72 NaN \n", - "73 NaN \n", - "74 NaN \n", - "75 NaN \n", - "76 NaN \n", - "77 NaN \n", - "78 NaN \n", - "79 NaN \n", - "80 NaN \n", - "81 NaN \n", - "82 NaN \n", - "83 NaN \n", - "84 NaN \n", - "85 NaN \n", - "86 NaN \n", - "87 NaN \n", - "88 NaN \n", - "89 NaN \n", - "90 NaN \n", - "91 NaN \n", - "92 NaN \n", - "93 NaN \n", - "94 NaN \n", - "95 NaN \n", - "96 NaN \n", - "97 NaN \n", - "98 NaN \n", - "99 NaN \n", - "100 NaN \n", - "101 NaN \n", - "102 NaN \n", - "103 NaN \n", - "104 NaN \n", - "105 NaN \n", - "106 NaN \n", - "107 NaN \n", - "108 NaN \n", - "109 NaN \n", - "110 NaN \n", - "111 NaN \n", - "112 NaN \n", - "113 NaN \n", - "114 NaN \n", - "115 NaN \n", - "116 NaN \n", - "117 NaN \n", - "118 NaN \n", - "119 NaN \n", - "120 NaN \n", - "121 NaN \n", - "122 NaN \n", - "\n", - " dav text-davinci-003.narrative synopsis \\\n", - "0 NaN \n", - "1 1.0 \n", - "2 5.0 \n", - "3 3.0 \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 NaN \n", - "8 NaN \n", - "9 NaN \n", - "10 NaN \n", - "11 NaN \n", - "12 NaN \n", - "13 NaN \n", - "14 NaN \n", - "15 NaN \n", - "16 NaN \n", - "17 NaN \n", - "18 NaN \n", - "19 NaN \n", - "20 NaN \n", - "21 NaN \n", - "22 NaN \n", - "23 NaN \n", - "24 NaN \n", - "25 NaN \n", - "26 NaN \n", - "27 NaN \n", - "28 NaN \n", - "29 NaN \n", - "30 NaN \n", - "31 NaN \n", - "32 NaN \n", - "33 NaN \n", - "34 NaN \n", - "35 NaN \n", - "36 NaN \n", - "37 NaN \n", - "38 NaN \n", - "39 NaN \n", - "40 NaN \n", - "41 NaN \n", - "42 NaN \n", - "43 NaN \n", - "44 NaN \n", - "45 NaN \n", - "46 NaN \n", - "47 NaN \n", - "48 2.0 \n", - "49 NaN \n", - "50 NaN \n", - "51 NaN \n", - "52 NaN \n", - "53 NaN \n", - "54 NaN \n", - "55 NaN \n", - "56 NaN \n", - "57 NaN \n", - "58 NaN \n", - "59 NaN \n", - "60 NaN \n", - "61 NaN \n", - "62 NaN \n", - "63 0.0 \n", - "64 4.0 \n", - "65 6.0 \n", - "66 NaN \n", - "67 NaN \n", - "68 NaN \n", - "69 NaN \n", - "70 NaN \n", - "71 NaN \n", - "72 NaN \n", - "73 NaN \n", - "74 NaN \n", - "75 NaN \n", - "76 NaN \n", - "77 NaN \n", - "78 NaN \n", - "79 NaN \n", - "80 NaN \n", - "81 NaN \n", - "82 NaN \n", - "83 NaN \n", - "84 NaN \n", - "85 NaN \n", - "86 NaN \n", - "87 NaN \n", - "88 NaN \n", - "89 NaN \n", - "90 NaN \n", - "91 NaN \n", - "92 NaN \n", - "93 NaN \n", - "94 NaN \n", - "95 NaN \n", - "96 NaN \n", - "97 NaN \n", - "98 NaN \n", - "99 NaN \n", - "100 NaN \n", - "101 NaN \n", - "102 NaN \n", - "103 NaN \n", - "104 NaN \n", - "105 NaN \n", - "106 NaN \n", - "107 NaN \n", - "108 NaN \n", - "109 NaN \n", - "110 NaN \n", - "111 NaN \n", - "112 NaN \n", - "113 NaN \n", - "114 NaN \n", - "115 NaN \n", - "116 NaN \n", - "117 NaN \n", - "118 NaN \n", - "119 NaN \n", - "120 NaN \n", - "121 NaN \n", - "122 NaN \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " id \\\n", + "0 GO:0006907 \n", + "1 GO:0006897 \n", + "2 GO:0044351 \n", + "3 GO:0016192 \n", + "4 GO:0030100 \n", + "5 GO:0006810 \n", + "6 GO:0051234 \n", + "7 GO:0045807 \n", + "8 GO:0060627 \n", + "9 GO:0051179 \n", + "10 GO:0031410 \n", + "11 GO:0097708 \n", + "12 GO:0050766 \n", + "13 GO:0048518 \n", + "14 GO:0050764 \n", + "15 GO:0051128 \n", + "16 GO:0031982 \n", + "17 GO:0150094 \n", + "18 GO:0006909 \n", + "19 GO:0051049 \n", + "20 GO:0006898 \n", + "21 GO:0051050 \n", + "22 GO:0005041 \n", + "23 GO:0030139 \n", + "24 GO:0030228 \n", + "25 GO:0030666 \n", + "26 GO:0097242 \n", + "27 GO:0051130 \n", + "28 GO:0060907 \n", + "29 GO:0032879 \n", + "30 GO:0048522 \n", + "31 GO:0002277 \n", + "32 GO:0030659 \n", + "33 GO:0012506 \n", + "34 GO:0061081 \n", + "35 GO:0010935 \n", + "36 GO:0048583 \n", + "37 GO:1905167 \n", + "38 GO:0009894 \n", + "39 GO:0023051 \n", + "40 GO:0051641 \n", + "41 GO:1904352 \n", + "42 GO:0070508 \n", + "43 GO:0031347 \n", + "44 GO:0005794 \n", + "45 GO:1901700 \n", + "46 GO:0061024 \n", + "47 GO:0009966 \n", + "48 GO:0015031 \n", + "49 GO:0048523 \n", + "50 GO:0032429 \n", + "51 GO:0034381 \n", + "52 GO:0031623 \n", + "53 GO:0009931 \n", + "54 GO:0005905 \n", + "55 GO:0032050 \n", + "56 GO:0030299 \n", + "57 GO:0001540 \n", + "58 GO:0034383 \n", + "59 GO:0032760 \n", + "60 GO:0042953 \n", + "61 GO:0071404 \n", + "62 GO:0030169 \n", + "63 GO:0055085 \n", + "64 GO:0043001 \n", + "65 macromolecule transport \n", + "66 GO:0000165 \n", + "67 intracellular trafficking \n", + "68 GO:0003924 \n", + "69 intracellular signaling \n", + "70 cytoskeletal reorganization \n", + "71 GO:0055088 \n", + "72 lysosomal degradation \n", + "73 the results of the term enrichment test on the list of given genes indicate that the genes are predominately involved in cellular processes such as intracellular signaling \n", + "74 cellular organization \n", + "75 GO:0005515 \n", + "76 GO:0051260 \n", + "77 and macropinocytosis. the predominant underlying pathway is protein transport \n", + "78 from the endoplasmic reticulum to the golgi \n", + "79 the nucleus \n", + "80 and then the plasma membrane. the enriched terms include ‘protein transport’ \n", + "81 'lysosomal degradation' \n", + "82 'endocytosis' \n", + "83 'cellular organization' \n", + "84 'protein binding' \n", + "85 ‘cellular signaling’ \n", + "86 ‘gtpase activity’ \n", + "87 ‘macropinocytosis’ \n", + "88 and ‘protein homooligomerization’. \\n\\nsummary: genes are predominantly involved in cellular processes such as intracellular signaling \n", + "89 and macropinocytosis. \\n\\nmechanism: protein transport from the endoplasmic reticulum to the golgi \n", + "90 GO:0005634 \n", + "91 then the plasma membrane. \\n\\nenriched \n", + "92 GO:0004672 \n", + "93 GO:0000902 \n", + "94 GO:0048468 \n", + "95 GO:0007155 \n", + "96 GO:0010467 \n", + "97 protein-protein interaction \n", + "98 gtp-dependent protein binding activity \n", + "99 GO:0004674 \n", + "100 protein ubiquitin ligase activity \n", + "101 GO:0032880 \n", + "102 signaling receptor binding activity \n", + "103 t cell receptor binding activity \n", + "104 GO:0005739 \n", + "105 GO:0006357 \n", + "106 GO:0005615 \n", + "107 GO:0003723 \n", + "108 GO:0070062 \n", + "109 GO:0005524 \n", + "110 GO:0042802 \n", + "111 GO:0005829 \n", + "112 GO:0005654 \n", + "113 GO:0005737 \n", + "114 GO:0016020 \n", + "115 GO:0005886 \n", + "116 GO:0005576 \n", + "117 GO:0045944 \n", + "118 GO:0046872 \n", "\n", - " dav text-davinci-003.ontological synopsis \\\n", - "0 NaN \n", - "1 13.0 \n", - "2 3.0 \n", - "3 NaN \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 NaN \n", - "8 NaN \n", - "9 NaN \n", - "10 NaN \n", - "11 NaN \n", - "12 NaN \n", - "13 NaN \n", - "14 NaN \n", - "15 NaN \n", - "16 NaN \n", - "17 NaN \n", - "18 10.0 \n", - "19 NaN \n", - "20 NaN \n", - "21 NaN \n", - "22 NaN \n", - "23 NaN \n", - "24 NaN \n", - "25 NaN \n", - "26 NaN \n", - "27 NaN \n", - "28 NaN \n", - "29 NaN \n", - "30 NaN \n", - "31 NaN \n", - "32 NaN \n", - "33 NaN \n", - "34 NaN \n", - "35 NaN \n", - "36 NaN \n", - "37 NaN \n", - "38 NaN \n", - "39 NaN \n", - "40 NaN \n", - "41 NaN \n", - "42 NaN \n", - "43 NaN \n", - "44 NaN \n", - "45 NaN \n", - "46 NaN \n", - "47 NaN \n", - "48 NaN \n", - "49 NaN \n", - "50 NaN \n", - "51 NaN \n", - "52 NaN \n", - "53 NaN \n", - "54 NaN \n", - "55 NaN \n", - "56 NaN \n", - "57 NaN \n", - "58 NaN \n", - "59 NaN \n", - "60 NaN \n", - "61 NaN \n", - "62 NaN \n", - "63 NaN \n", - "64 NaN \n", - "65 NaN \n", - "66 NaN \n", - "67 NaN \n", - "68 NaN \n", - "69 NaN \n", - "70 0.0 \n", - "71 1.0 \n", - "72 2.0 \n", - "73 4.0 \n", - "74 5.0 \n", - "75 6.0 \n", - "76 7.0 \n", - "77 8.0 \n", - "78 9.0 \n", - "79 11.0 \n", - "80 12.0 \n", - "81 14.0 \n", - "82 15.0 \n", - "83 NaN \n", - "84 NaN \n", - "85 NaN \n", - "86 NaN \n", - "87 NaN \n", - "88 NaN \n", - "89 NaN \n", - "90 NaN \n", - "91 NaN \n", - "92 NaN \n", - "93 NaN \n", - "94 NaN \n", - "95 NaN \n", - "96 NaN \n", - "97 NaN \n", - "98 NaN \n", - "99 NaN \n", - "100 NaN \n", - "101 NaN \n", - "102 NaN \n", - "103 NaN \n", - "104 NaN \n", - "105 NaN \n", - "106 NaN \n", - "107 NaN \n", - "108 NaN \n", - "109 NaN \n", - "110 NaN \n", - "111 NaN \n", - "112 NaN \n", - "113 NaN \n", - "114 NaN \n", - "115 NaN \n", - "116 NaN \n", - "117 NaN \n", - "118 NaN \n", - "119 NaN \n", - "120 NaN \n", - "121 NaN \n", - "122 NaN \n", + " label \\\n", + "0 pinocytosis \n", + "1 endocytosis \n", + "2 macropinocytosis \n", + "3 vesicle-mediated transport \n", + "4 regulation of endocytosis \n", + "5 transport \n", + "6 establishment of localization \n", + "7 positive regulation of endocytosis \n", + "8 regulation of vesicle-mediated transport \n", + "9 localization \n", + "10 cytoplasmic vesicle \n", + "11 intracellular vesicle \n", + "12 positive regulation of phagocytosis \n", + "13 positive regulation of biological process \n", + "14 regulation of phagocytosis \n", + "15 regulation of cellular component organization \n", + "16 vesicle \n", + "17 amyloid-beta clearance by cellular catabolic process \n", + "18 phagocytosis \n", + "19 regulation of transport \n", + "20 receptor-mediated endocytosis \n", + "21 positive regulation of transport \n", + "22 low-density lipoprotein particle receptor activity \n", + "23 endocytic vesicle \n", + "24 lipoprotein particle receptor activity \n", + "25 endocytic vesicle membrane \n", + "26 amyloid-beta clearance \n", + "27 positive regulation of cellular component organization \n", + "28 positive regulation of macrophage cytokine production \n", + "29 regulation of localization \n", + "30 positive regulation of cellular process \n", + "31 myeloid dendritic cell activation involved in immune response \n", + "32 cytoplasmic vesicle membrane \n", + "33 vesicle membrane \n", + "34 positive regulation of myeloid leukocyte cytokine production involved in immune response \n", + "35 regulation of macrophage cytokine production \n", + "36 regulation of response to stimulus \n", + "37 positive regulation of lysosomal protein catabolic process \n", + "38 regulation of catabolic process \n", + "39 regulation of signaling \n", + "40 cellular localization \n", + "41 positive regulation of protein catabolic process in the vacuole \n", + "42 cholesterol import \n", + "43 regulation of defense response \n", + "44 Golgi apparatus \n", + "45 response to oxygen-containing compound \n", + "46 membrane organization \n", + "47 regulation of signal transduction \n", + "48 protein transport \n", + "49 negative regulation of cellular process \n", + "50 regulation of phospholipase A2 activity \n", + "51 plasma lipoprotein particle clearance \n", + "52 receptor internalization \n", + "53 calcium-dependent protein serine/threonine kinase activity \n", + "54 clathrin-coated pit \n", + "55 clathrin heavy chain binding \n", + "56 intestinal cholesterol absorption \n", + "57 amyloid-beta binding \n", + "58 low-density lipoprotein particle clearance \n", + "59 positive regulation of tumor necrosis factor production \n", + "60 lipoprotein transport \n", + "61 cellular response to low-density lipoprotein particle stimulus \n", + "62 low-density lipoprotein particle binding \n", + "63 transmembrane transport \n", + "64 Golgi to plasma membrane protein transport \n", + "65 None \n", + "66 MAPK cascade \n", + "67 None \n", + "68 GTPase activity \n", + "69 None \n", + "70 None \n", + "71 lipid homeostasis \n", + "72 None \n", + "73 None \n", + "74 None \n", + "75 protein binding \n", + "76 protein homooligomerization \n", + "77 None \n", + "78 None \n", + "79 None \n", + "80 None \n", + "81 None \n", + "82 None \n", + "83 None \n", + "84 None \n", + "85 None \n", + "86 None \n", + "87 None \n", + "88 None \n", + "89 None \n", + "90 nucleus \n", + "91 None \n", + "92 protein kinase activity \n", + "93 cell morphogenesis \n", + "94 cell development \n", + "95 cell adhesion \n", + "96 gene expression \n", + "97 None \n", + "98 None \n", + "99 protein serine/threonine kinase activity \n", + "100 None \n", + "101 regulation of protein localization \n", + "102 None \n", + "103 None \n", + "104 mitochondrion \n", + "105 regulation of transcription by RNA polymerase II \n", + "106 extracellular space \n", + "107 RNA binding \n", + "108 extracellular exosome \n", + "109 ATP binding \n", + "110 identical protein binding \n", + "111 cytosol \n", + "112 nucleoplasm \n", + "113 cytoplasm \n", + "114 membrane \n", + "115 plasma membrane \n", + "116 extracellular region \n", + "117 positive regulation of transcription by RNA polymerase II \n", + "118 metal ion binding \n", "\n", - " turbo gpt-3.5-turbo.narrative synopsis turbo gpt-3.5-turbo.no synopsis \\\n", - "0 NaN NaN \n", - "1 0.0 1.0 \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "17 NaN NaN \n", - "18 NaN NaN \n", - "19 NaN NaN \n", - "20 NaN NaN \n", - "21 NaN NaN \n", - "22 NaN NaN \n", - "23 NaN NaN \n", - "24 NaN NaN \n", - "25 NaN NaN \n", - "26 NaN NaN \n", - "27 NaN NaN \n", - "28 NaN NaN \n", - "29 NaN NaN \n", - "30 NaN NaN \n", - "31 NaN NaN \n", - "32 NaN NaN \n", - "33 NaN NaN \n", - "34 NaN NaN \n", - "35 NaN NaN \n", - "36 NaN NaN \n", - "37 NaN NaN \n", - "38 NaN NaN \n", - "39 NaN NaN \n", - "40 NaN NaN \n", - "41 NaN NaN \n", - "42 NaN NaN \n", - "43 NaN NaN \n", - "44 NaN NaN \n", - "45 NaN NaN \n", - "46 NaN NaN \n", - "47 NaN NaN \n", - "48 NaN NaN \n", - "49 NaN NaN \n", - "50 NaN NaN \n", - "51 NaN NaN \n", - "52 NaN NaN \n", - "53 NaN NaN \n", - "54 NaN NaN \n", - "55 NaN NaN \n", - "56 NaN NaN \n", - "57 NaN NaN \n", - "58 NaN NaN \n", - "59 NaN NaN \n", - "60 NaN NaN \n", - "61 NaN NaN \n", - "62 NaN NaN \n", - "63 NaN NaN \n", - "64 NaN NaN \n", - "65 NaN NaN \n", - "66 2.0 NaN \n", - "67 NaN NaN \n", - "68 NaN NaN \n", - "69 NaN NaN \n", - "70 NaN NaN \n", - "71 NaN NaN \n", - "72 NaN NaN \n", - "73 NaN NaN \n", - "74 NaN NaN \n", - "75 NaN NaN \n", - "76 NaN NaN \n", - "77 NaN NaN \n", - "78 NaN NaN \n", - "79 NaN NaN \n", - "80 NaN NaN \n", - "81 NaN NaN \n", - "82 NaN NaN \n", - "83 1.0 NaN \n", - "84 NaN 0.0 \n", - "85 NaN 2.0 \n", - "86 NaN NaN \n", - "87 NaN NaN \n", - "88 NaN NaN \n", - "89 NaN NaN \n", - "90 NaN NaN \n", - "91 NaN NaN \n", - "92 NaN NaN \n", - "93 NaN NaN \n", - "94 NaN NaN \n", - "95 NaN NaN \n", - "96 NaN NaN \n", - "97 NaN NaN \n", - "98 NaN NaN \n", - "99 NaN NaN \n", - "100 NaN NaN \n", - "101 NaN NaN \n", - "102 NaN NaN \n", - "103 NaN NaN \n", - "104 NaN NaN \n", - "105 NaN NaN \n", - "106 NaN NaN \n", - "107 NaN NaN \n", - "108 NaN NaN \n", - "109 NaN NaN \n", - "110 NaN NaN \n", - "111 NaN NaN \n", - "112 NaN NaN \n", - "113 NaN NaN \n", - "114 NaN NaN \n", - "115 NaN NaN \n", - "116 NaN NaN \n", - "117 NaN NaN \n", - "118 NaN NaN \n", - "119 NaN NaN \n", - "120 NaN NaN \n", - "121 NaN NaN \n", - "122 NaN NaN \n", + " redundant standard standard no ontology turbo ontological synopsis \\\n", + "0 False 0.0 1.0 3.0 \n", + "1 True 1.0 6.0 0.0 \n", + "2 True 2.0 0.0 NaN \n", + "3 True 3.0 NaN NaN \n", + "4 False 4.0 NaN NaN \n", + "5 True 5.0 NaN NaN \n", + "6 True 6.0 NaN NaN \n", + "7 True 7.0 NaN NaN \n", + "8 True 8.0 NaN NaN \n", + "9 True 9.0 NaN NaN \n", + "10 False 10.0 NaN NaN \n", + "11 True 11.0 NaN NaN \n", + "12 True 12.0 4.0 NaN \n", + "13 True 13.0 NaN NaN \n", + "14 True 14.0 NaN NaN \n", + "15 True 15.0 NaN NaN \n", + "16 True 16.0 NaN NaN \n", + "17 False 17.0 3.0 NaN \n", + "18 True 18.0 17.0 NaN \n", + "19 True 19.0 NaN NaN \n", + "20 True 20.0 2.0 4.0 \n", + "21 True 21.0 NaN NaN \n", + "22 True 22.0 5.0 NaN \n", + "23 True 23.0 NaN NaN \n", + "24 True 24.0 NaN NaN \n", + "25 True 25.0 18.0 NaN \n", + "26 True 26.0 22.0 NaN \n", + "27 True 27.0 NaN NaN \n", + "28 True 28.0 8.0 NaN \n", + "29 True 29.0 NaN NaN \n", + "30 True 30.0 NaN NaN \n", + "31 False 31.0 9.0 NaN \n", + "32 True 32.0 NaN NaN \n", + "33 True 33.0 NaN NaN \n", + "34 True 34.0 NaN NaN \n", + "35 True 35.0 NaN NaN \n", + "36 False 36.0 NaN NaN \n", + "37 True 37.0 10.0 NaN \n", + "38 True 38.0 NaN NaN \n", + "39 False 39.0 NaN NaN \n", + "40 True 40.0 NaN NaN \n", + "41 True 41.0 NaN NaN \n", + "42 True 42.0 13.0 NaN \n", + "43 True 43.0 NaN NaN \n", + "44 False 44.0 14.0 NaN \n", + "45 False 45.0 NaN NaN \n", + "46 False 46.0 NaN NaN \n", + "47 True 47.0 NaN NaN \n", + "48 True 48.0 NaN NaN \n", + "49 False 49.0 NaN NaN \n", + "50 False NaN 7.0 NaN \n", + "51 False NaN 11.0 NaN \n", + "52 False NaN 12.0 NaN \n", + "53 False NaN 15.0 NaN \n", + "54 False NaN 16.0 NaN \n", + "55 False NaN 19.0 NaN \n", + "56 False NaN 20.0 NaN \n", + "57 False NaN 21.0 NaN \n", + "58 False NaN 23.0 NaN \n", + "59 False NaN 24.0 NaN \n", + "60 False NaN 25.0 NaN \n", + "61 False NaN 26.0 NaN \n", + "62 False NaN 27.0 NaN \n", + "63 False NaN NaN 1.0 \n", + "64 False NaN NaN 2.0 \n", + "65 False NaN NaN NaN \n", + "66 False NaN NaN NaN \n", + "67 False NaN NaN NaN \n", + "68 False NaN NaN NaN \n", + "69 False NaN NaN NaN \n", + "70 False NaN NaN NaN \n", + "71 False NaN NaN NaN \n", + "72 False NaN NaN NaN \n", + "73 False NaN NaN NaN \n", + "74 False NaN NaN NaN \n", + "75 False NaN NaN NaN \n", + "76 False NaN NaN NaN \n", + "77 False NaN NaN NaN \n", + "78 False NaN NaN NaN \n", + "79 False NaN NaN NaN \n", + "80 False NaN NaN NaN \n", + "81 False NaN NaN NaN \n", + "82 False NaN NaN NaN \n", + "83 False NaN NaN NaN \n", + "84 False NaN NaN NaN \n", + "85 False NaN NaN NaN \n", + "86 False NaN NaN NaN \n", + "87 False NaN NaN NaN \n", + "88 False NaN NaN NaN \n", + "89 False NaN NaN NaN \n", + "90 False NaN NaN NaN \n", + "91 False NaN NaN NaN \n", + "92 False NaN NaN NaN \n", + "93 False NaN NaN NaN \n", + "94 False NaN NaN NaN \n", + "95 False NaN NaN NaN \n", + "96 False NaN NaN NaN \n", + "97 False NaN NaN NaN \n", + "98 False NaN NaN NaN \n", + "99 False NaN NaN NaN \n", + "100 False NaN NaN NaN \n", + "101 False NaN NaN NaN \n", + "102 False NaN NaN NaN \n", + "103 False NaN NaN NaN \n", + "104 False NaN NaN NaN \n", + "105 False NaN NaN NaN \n", + "106 False NaN NaN NaN \n", + "107 False NaN NaN NaN \n", + "108 False NaN NaN NaN \n", + "109 False NaN NaN NaN \n", + "110 False NaN NaN NaN \n", + "111 False NaN NaN NaN \n", + "112 False NaN NaN NaN \n", + "113 False NaN NaN NaN \n", + "114 False NaN NaN NaN \n", + "115 False NaN NaN NaN \n", + "116 False NaN NaN NaN \n", + "117 False NaN NaN NaN \n", + "118 False NaN NaN NaN \n", "\n", - " rank based dav text-davinci-003.no synopsis random \n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "17 NaN NaN NaN \n", - "18 NaN NaN NaN \n", - "19 NaN NaN NaN \n", - "20 NaN NaN NaN \n", - "21 NaN NaN NaN \n", - "22 NaN NaN NaN \n", - "23 NaN NaN NaN \n", - "24 NaN NaN NaN \n", - "25 NaN NaN NaN \n", - "26 NaN NaN NaN \n", - "27 NaN NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 NaN NaN NaN \n", - "31 NaN NaN NaN \n", - "32 NaN NaN NaN \n", - "33 NaN NaN NaN \n", - "34 NaN NaN NaN \n", - "35 NaN NaN NaN \n", - "36 NaN NaN NaN \n", - "37 NaN NaN NaN \n", - "38 NaN NaN NaN \n", - "39 NaN NaN NaN \n", - "40 NaN NaN NaN \n", - "41 NaN NaN NaN \n", - "42 NaN NaN NaN \n", - "43 NaN NaN NaN \n", - "44 NaN NaN NaN \n", - "45 NaN NaN NaN \n", - "46 NaN NaN NaN \n", - "47 NaN NaN NaN \n", - "48 NaN NaN NaN \n", - "49 NaN NaN NaN \n", - "50 NaN NaN NaN \n", - "51 NaN NaN NaN \n", - "52 NaN NaN NaN \n", - "53 NaN NaN NaN \n", - "54 NaN NaN NaN \n", - "55 NaN NaN NaN \n", - "56 NaN NaN NaN \n", - "57 NaN NaN NaN \n", - "58 NaN NaN NaN \n", - "59 NaN NaN NaN \n", - "60 NaN NaN NaN \n", - "61 NaN NaN NaN \n", - "62 NaN NaN NaN \n", - "63 NaN NaN NaN \n", - "64 NaN NaN NaN \n", - "65 NaN NaN NaN \n", - "66 NaN NaN NaN \n", - "67 NaN NaN NaN \n", - "68 NaN NaN NaN \n", - "69 NaN NaN NaN \n", - "70 NaN NaN NaN \n", - "71 11.0 NaN NaN \n", - "72 NaN NaN NaN \n", - "73 NaN NaN NaN \n", - "74 NaN NaN NaN \n", - "75 NaN NaN NaN \n", - "76 NaN NaN NaN \n", - "77 NaN NaN NaN \n", - "78 NaN NaN NaN \n", - "79 NaN NaN NaN \n", - "80 NaN NaN NaN \n", - "81 NaN NaN NaN \n", - "82 NaN NaN NaN \n", - "83 NaN NaN NaN \n", - "84 NaN NaN NaN \n", - "85 NaN NaN NaN \n", - "86 NaN 0.0 NaN \n", - "87 NaN 1.0 NaN \n", - "88 NaN 2.0 NaN \n", - "89 NaN 3.0 NaN \n", - "90 NaN 4.0 NaN \n", - "91 NaN 5.0 NaN \n", - "92 NaN 6.0 NaN \n", - "93 NaN NaN 0.0 \n", - "94 NaN NaN 1.0 \n", - "95 NaN NaN 2.0 \n", - "96 NaN NaN 3.0 \n", - "97 NaN NaN 4.0 \n", - "98 NaN NaN 5.0 \n", - "99 NaN NaN 6.0 \n", - "100 NaN NaN 7.0 \n", - "101 NaN NaN 8.0 \n", - "102 NaN NaN 9.0 \n", - "103 NaN NaN 10.0 \n", - "104 13.0 NaN 11.0 \n", - "105 NaN NaN 12.0 \n", - "106 NaN NaN 13.0 \n", - "107 NaN NaN 14.0 \n", - "108 NaN NaN 15.0 \n", - "109 0.0 NaN NaN \n", - "110 1.0 NaN NaN \n", - "111 2.0 NaN NaN \n", - "112 3.0 NaN NaN \n", - "113 4.0 NaN NaN \n", - "114 5.0 NaN NaN \n", - "115 6.0 NaN NaN \n", - "116 7.0 NaN NaN \n", - "117 8.0 NaN NaN \n", - "118 9.0 NaN NaN \n", - "119 10.0 NaN NaN \n", - "120 12.0 NaN NaN \n", - "121 14.0 NaN NaN \n", - "122 15.0 NaN NaN " + " turbo no synopsis turbo narrative synopsis dav narrative synopsis \\\n", + "0 NaN NaN NaN \n", + "1 0.0 0.0 20.0 \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "17 NaN NaN NaN \n", + "18 NaN NaN NaN \n", + "19 NaN NaN NaN \n", + "20 1.0 NaN NaN \n", + "21 NaN NaN NaN \n", + "22 NaN NaN NaN \n", + "23 NaN NaN NaN \n", + "24 NaN NaN NaN \n", + "25 NaN NaN NaN \n", + "26 NaN NaN NaN \n", + "27 NaN NaN NaN \n", + "28 NaN NaN NaN \n", + "29 NaN NaN NaN \n", + "30 NaN NaN NaN \n", + "31 NaN NaN NaN \n", + "32 NaN NaN NaN \n", + "33 NaN NaN NaN \n", + "34 NaN NaN NaN \n", + "35 NaN NaN NaN \n", + "36 NaN NaN NaN \n", + "37 NaN NaN NaN \n", + "38 NaN NaN NaN \n", + "39 NaN NaN NaN \n", + "40 NaN NaN NaN \n", + "41 NaN NaN NaN \n", + "42 NaN NaN NaN \n", + "43 NaN NaN NaN \n", + "44 NaN NaN NaN \n", + "45 NaN NaN NaN \n", + "46 NaN NaN NaN \n", + "47 NaN NaN NaN \n", + "48 NaN 2.0 18.0 \n", + "49 NaN NaN NaN \n", + "50 NaN NaN NaN \n", + "51 NaN NaN NaN \n", + "52 NaN NaN NaN \n", + "53 NaN NaN NaN \n", + "54 NaN NaN NaN \n", + "55 NaN NaN NaN \n", + "56 NaN NaN NaN \n", + "57 NaN NaN NaN \n", + "58 NaN NaN NaN \n", + "59 NaN NaN NaN \n", + "60 NaN NaN NaN \n", + "61 NaN NaN NaN \n", + "62 NaN NaN NaN \n", + "63 NaN NaN NaN \n", + "64 NaN NaN NaN \n", + "65 2.0 NaN NaN \n", + "66 3.0 NaN NaN \n", + "67 4.0 NaN NaN \n", + "68 NaN 1.0 NaN \n", + "69 NaN 3.0 NaN \n", + "70 NaN 4.0 NaN \n", + "71 NaN 5.0 NaN \n", + "72 NaN 6.0 19.0 \n", + "73 NaN NaN 0.0 \n", + "74 NaN NaN 22.0 \n", + "75 NaN NaN 21.0 \n", + "76 NaN NaN 4.0 \n", + "77 NaN NaN 6.0 \n", + "78 NaN NaN 7.0 \n", + "79 NaN NaN 8.0 \n", + "80 NaN NaN 9.0 \n", + "81 NaN NaN 10.0 \n", + "82 NaN NaN 11.0 \n", + "83 NaN NaN 12.0 \n", + "84 NaN NaN 13.0 \n", + "85 NaN NaN 14.0 \n", + "86 NaN NaN 15.0 \n", + "87 NaN NaN 16.0 \n", + "88 NaN NaN 17.0 \n", + "89 NaN NaN 23.0 \n", + "90 NaN NaN 24.0 \n", + "91 NaN NaN 25.0 \n", + "92 NaN NaN NaN \n", + "93 NaN NaN NaN \n", + "94 NaN NaN NaN \n", + "95 NaN NaN NaN \n", + "96 NaN NaN NaN \n", + "97 NaN NaN NaN \n", + "98 NaN NaN NaN \n", + "99 NaN NaN NaN \n", + "100 NaN NaN NaN \n", + "101 NaN NaN NaN \n", + "102 NaN NaN NaN \n", + "103 NaN NaN NaN \n", + "104 NaN NaN NaN \n", + "105 NaN NaN NaN \n", + "106 NaN NaN NaN \n", + "107 NaN NaN NaN \n", + "108 NaN NaN NaN \n", + "109 NaN NaN NaN \n", + "110 NaN NaN NaN \n", + "111 NaN NaN NaN \n", + "112 NaN NaN NaN \n", + "113 NaN NaN NaN \n", + "114 NaN NaN NaN \n", + "115 NaN NaN NaN \n", + "116 NaN NaN NaN \n", + "117 NaN NaN NaN \n", + "118 NaN NaN NaN \n", + "\n", + " dav no synopsis rank based dav ontological synopsis \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "17 NaN NaN NaN \n", + "18 NaN NaN NaN \n", + "19 NaN NaN NaN \n", + "20 NaN NaN NaN \n", + "21 NaN NaN NaN \n", + "22 NaN NaN NaN \n", + "23 NaN NaN NaN \n", + "24 NaN NaN NaN \n", + "25 NaN NaN NaN \n", + "26 NaN NaN NaN \n", + "27 NaN NaN NaN \n", + "28 NaN NaN NaN \n", + "29 NaN NaN NaN \n", + "30 NaN NaN NaN \n", + "31 NaN NaN NaN \n", + "32 NaN NaN NaN \n", + "33 NaN NaN NaN \n", + "34 NaN NaN NaN \n", + "35 NaN NaN NaN \n", + "36 NaN NaN NaN \n", + "37 NaN NaN NaN \n", + "38 NaN NaN NaN \n", + "39 NaN NaN NaN \n", + "40 NaN NaN NaN \n", + "41 NaN NaN NaN \n", + "42 NaN NaN NaN \n", + "43 NaN NaN NaN \n", + "44 NaN NaN NaN \n", + "45 NaN NaN NaN \n", + "46 NaN NaN NaN \n", + "47 NaN NaN NaN \n", + "48 NaN NaN NaN \n", + "49 NaN NaN NaN \n", + "50 NaN NaN NaN \n", + "51 NaN NaN NaN \n", + "52 NaN NaN NaN \n", + "53 NaN NaN NaN \n", + "54 NaN NaN NaN \n", + "55 NaN NaN NaN \n", + "56 NaN NaN NaN \n", + "57 NaN NaN NaN \n", + "58 NaN NaN NaN \n", + "59 NaN NaN NaN \n", + "60 NaN NaN NaN \n", + "61 NaN NaN NaN \n", + "62 NaN NaN NaN \n", + "63 NaN NaN NaN \n", + "64 NaN NaN NaN \n", + "65 NaN NaN NaN \n", + "66 NaN NaN NaN \n", + "67 NaN NaN NaN \n", + "68 NaN NaN NaN \n", + "69 1.0 NaN NaN \n", + "70 NaN NaN NaN \n", + "71 NaN NaN NaN \n", + "72 NaN NaN NaN \n", + "73 NaN NaN NaN \n", + "74 NaN NaN NaN \n", + "75 NaN NaN NaN \n", + "76 NaN NaN NaN \n", + "77 NaN NaN NaN \n", + "78 NaN NaN NaN \n", + "79 NaN NaN NaN \n", + "80 NaN NaN NaN \n", + "81 NaN NaN NaN \n", + "82 NaN NaN NaN \n", + "83 NaN NaN NaN \n", + "84 NaN NaN NaN \n", + "85 NaN NaN NaN \n", + "86 NaN NaN NaN \n", + "87 NaN NaN NaN \n", + "88 NaN NaN NaN \n", + "89 NaN NaN NaN \n", + "90 NaN 4.0 NaN \n", + "91 NaN NaN NaN \n", + "92 0.0 NaN NaN \n", + "93 2.0 NaN NaN \n", + "94 3.0 NaN NaN \n", + "95 4.0 NaN NaN \n", + "96 5.0 NaN NaN \n", + "97 6.0 NaN NaN \n", + "98 NaN NaN 0.0 \n", + "99 NaN NaN 1.0 \n", + "100 NaN NaN 2.0 \n", + "101 NaN NaN 3.0 \n", + "102 NaN NaN 4.0 \n", + "103 NaN NaN 5.0 \n", + "104 NaN 0.0 NaN \n", + "105 NaN 1.0 NaN \n", + "106 NaN 2.0 NaN \n", + "107 NaN 3.0 NaN \n", + "108 NaN 5.0 NaN \n", + "109 NaN 6.0 NaN \n", + "110 NaN 7.0 NaN \n", + "111 NaN 8.0 NaN \n", + "112 NaN 9.0 NaN \n", + "113 NaN 10.0 NaN \n", + "114 NaN 11.0 NaN \n", + "115 NaN 12.0 NaN \n", + "116 NaN 13.0 NaN \n", + "117 NaN 14.0 NaN \n", + "118 NaN 15.0 NaN " ] }, - "execution_count": 35, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } diff --git a/src/ontogpt/engines/enrichment.py b/src/ontogpt/engines/enrichment.py index 8cb668abc..13d92ad3e 100644 --- a/src/ontogpt/engines/enrichment.py +++ b/src/ontogpt/engines/enrichment.py @@ -5,7 +5,7 @@ from dataclasses import dataclass, field from enum import Enum from pathlib import Path -from typing import Dict, List, Optional, Tuple +from typing import Dict, List, Optional, Tuple, Union from jinja2 import Template from oaklib import BasicOntologyInterface, get_adapter @@ -120,9 +120,9 @@ def summarize( raise NotImplementedError if not gene_set.gene_ids and not gene_set.gene_symbols: raise ValueError(f"Gene set {gene_set.name} has no gene symbols or ids") - if gene_set.gene_ids and not gene_set.gene_symbols: - adapter = list(self.label_resolvers.values())[0] - gene_set.gene_symbols = [adapter.label(x.lower()) for x in gene_set.gene_ids] + # if gene_set.gene_ids and not gene_set.gene_symbols: + # adapter = list(self.label_resolvers.values())[0] + # gene_set.gene_symbols = [adapter.label(x.lower()) for x in gene_set.gene_ids] if not gene_set.gene_ids or normalize: gene_set.gene_ids = list(self.map_labels(gene_set.gene_symbols, strict=strict)) logger.info(f"gene ids: {gene_set.gene_ids}") @@ -152,7 +152,10 @@ def summarize( if not prompt_template: prompt_template = str(f"{DEFAULT_ENRICHMENT_PROMPT}.jinja2") prompt, tf = self._prompt_from_template( - gene_tuples, template=prompt_template, annotations=annotations + gene_tuples, + template=prompt_template, + annotations=annotations, + taxon=gene_set.taxon, ) response_text = self.client.complete(prompt, max_tokens=self.completion_length) response_token_length = len(self.encoding.encode(response_text)) @@ -173,8 +176,9 @@ def summarize( def _prompt_from_template( self, genes: List[GENE_TUPLE], - template: str, + template: Union[str, Path, Template], truncation_factor=1.0, + taxon: str = None, annotations=True, ) -> Tuple[str, float]: if isinstance(template, Path): @@ -195,6 +199,7 @@ def _prompt_from_template( prompt = template.render( gene_descriptions=gd_tuples, annotations=annotations, + taxon=taxon, SUMMARY_KEYWORD=SUMMARY_KEYWORD, MECHANISM_KEYWORD=MECHANISM_KEYWORD, ENRICHED_TERMS_KEYWORD=ENRICHED_TERMS_KEYWORD, @@ -202,15 +207,18 @@ def _prompt_from_template( logging.debug(f"Prompt from template: {prompt}") logging.info(f"Prompt [{truncation_factor}] Length: {len(prompt)}") # https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them - prompt_length = len(self.encoding.encode(prompt)) - logging.info(f"Prompt [{truncation_factor}] Tokens: {prompt_length} Strlen={len(prompt)}") + prompt_length = len(self.encoding.encode(prompt)) + 10 max_len = 4097 - self.completion_length + logging.info( + f"Prompt [{truncation_factor}] Toks: {prompt_length} / {max_len} Str={len(prompt)}" + ) if prompt_length > max_len: # TODO: check this logging.warning(f"Prompt is too long; toks: {prompt_length} len: {len(prompt)}") return self._prompt_from_template( genes, template, truncation_factor=truncation_factor * 0.8, + taxon=taxon, annotations=annotations, ) return prompt, truncation_factor diff --git a/src/ontogpt/prompts/enrichment/gene_set_summarization.jinja2 b/src/ontogpt/prompts/enrichment/gene_set_summarization.jinja2 index e7cc2e478..c9d380780 100644 --- a/src/ontogpt/prompts/enrichment/gene_set_summarization.jinja2 +++ b/src/ontogpt/prompts/enrichment/gene_set_summarization.jinja2 @@ -1,8 +1,4 @@ -{% if annotations %} -I will give you a list of genes together with descriptions of their functions. -{% else %} -I will give you a list of genes. -{% endif %} +I will give you a list of {{ taxon }} genes {% if annotations %} together with descriptions of their functions{% endif %}. Perform a term enrichment test on these genes. i.e. tell me what the commonalities are in their function. Make use of classification hierarchies when you do this. diff --git a/src/ontogpt/utils/gene_set_utils.py b/src/ontogpt/utils/gene_set_utils.py index d4a24cf5e..bc67d2c15 100644 --- a/src/ontogpt/utils/gene_set_utils.py +++ b/src/ontogpt/utils/gene_set_utils.py @@ -26,7 +26,10 @@ class GeneSet(BaseModel): gene_symbols: Optional[List[str]] = None gene_ids: Optional[List[str]] = None taxon: str = "human" + taxon_id: Optional[str] = None description: Optional[str] = None + source: Optional[str] = None + source_url: Optional[str] = None target_term_ids: Optional[List[str]] = None diff --git a/tests/input/genesets/HALLMARK_ADIPOGENESIS.yaml b/tests/input/genesets/HALLMARK_ADIPOGENESIS.yaml index a84489728..8def526c0 100644 --- a/tests/input/genesets/HALLMARK_ADIPOGENESIS.yaml +++ b/tests/input/genesets/HALLMARK_ADIPOGENESIS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_ADIPOGENESIS -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCA1 - ABCB8 - ACAA2 diff --git a/tests/input/genesets/HALLMARK_ALLOGRAFT_REJECTION.yaml b/tests/input/genesets/HALLMARK_ALLOGRAFT_REJECTION.yaml index 375c30283..a22828c4a 100644 --- a/tests/input/genesets/HALLMARK_ALLOGRAFT_REJECTION.yaml +++ b/tests/input/genesets/HALLMARK_ALLOGRAFT_REJECTION.yaml @@ -1,6 +1,6 @@ name: HALLMARK_ALLOGRAFT_REJECTION -gene_symbols: [] -gene_ids: + +gene_symbols: - AARS1 - ABCE1 - ABI1 diff --git a/tests/input/genesets/HALLMARK_ANDROGEN_RESPONSE.yaml b/tests/input/genesets/HALLMARK_ANDROGEN_RESPONSE.yaml index 70b405980..3ea48b0c4 100644 --- a/tests/input/genesets/HALLMARK_ANDROGEN_RESPONSE.yaml +++ b/tests/input/genesets/HALLMARK_ANDROGEN_RESPONSE.yaml @@ -1,6 +1,6 @@ name: HALLMARK_ANDROGEN_RESPONSE -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCC4 - ABHD2 - ACSL3 diff --git a/tests/input/genesets/HALLMARK_ANGIOGENESIS.yaml b/tests/input/genesets/HALLMARK_ANGIOGENESIS.yaml index f66bc54ee..6a2efd5a6 100644 --- a/tests/input/genesets/HALLMARK_ANGIOGENESIS.yaml +++ b/tests/input/genesets/HALLMARK_ANGIOGENESIS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_ANGIOGENESIS -gene_symbols: [] -gene_ids: + +gene_symbols: - APOH - APP - CCND2 diff --git a/tests/input/genesets/HALLMARK_APICAL_JUNCTION.yaml b/tests/input/genesets/HALLMARK_APICAL_JUNCTION.yaml index 7758987d7..71d43aae3 100644 --- a/tests/input/genesets/HALLMARK_APICAL_JUNCTION.yaml +++ b/tests/input/genesets/HALLMARK_APICAL_JUNCTION.yaml @@ -1,6 +1,6 @@ name: HALLMARK_APICAL_JUNCTION -gene_symbols: [] -gene_ids: + +gene_symbols: - ACTA1 - ACTB - ACTC1 diff --git a/tests/input/genesets/HALLMARK_APICAL_SURFACE.yaml b/tests/input/genesets/HALLMARK_APICAL_SURFACE.yaml index f8d9e80f6..a74f85f9e 100644 --- a/tests/input/genesets/HALLMARK_APICAL_SURFACE.yaml +++ b/tests/input/genesets/HALLMARK_APICAL_SURFACE.yaml @@ -1,6 +1,6 @@ name: HALLMARK_APICAL_SURFACE -gene_symbols: [] -gene_ids: + +gene_symbols: - ADAM10 - ADIPOR2 - AFAP1L2 diff --git a/tests/input/genesets/HALLMARK_APOPTOSIS.yaml b/tests/input/genesets/HALLMARK_APOPTOSIS.yaml index 297ed43e8..32e5ed03f 100644 --- a/tests/input/genesets/HALLMARK_APOPTOSIS.yaml +++ b/tests/input/genesets/HALLMARK_APOPTOSIS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_APOPTOSIS -gene_symbols: [] -gene_ids: + +gene_symbols: - ADD1 - AIFM3 - ANKH diff --git a/tests/input/genesets/HALLMARK_BILE_ACID_METABOLISM.yaml b/tests/input/genesets/HALLMARK_BILE_ACID_METABOLISM.yaml index f4f9fd3e5..82f195fc8 100644 --- a/tests/input/genesets/HALLMARK_BILE_ACID_METABOLISM.yaml +++ b/tests/input/genesets/HALLMARK_BILE_ACID_METABOLISM.yaml @@ -1,6 +1,6 @@ name: HALLMARK_BILE_ACID_METABOLISM -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCA1 - ABCA2 - ABCA3 diff --git a/tests/input/genesets/HALLMARK_CHOLESTEROL_HOMEOSTASIS.yaml b/tests/input/genesets/HALLMARK_CHOLESTEROL_HOMEOSTASIS.yaml index e40bd85ee..3fdf816f6 100644 --- a/tests/input/genesets/HALLMARK_CHOLESTEROL_HOMEOSTASIS.yaml +++ b/tests/input/genesets/HALLMARK_CHOLESTEROL_HOMEOSTASIS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_CHOLESTEROL_HOMEOSTASIS -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCA2 - ACAT2 - ACSS2 diff --git a/tests/input/genesets/HALLMARK_COAGULATION.yaml b/tests/input/genesets/HALLMARK_COAGULATION.yaml index 590619ab9..e507bc152 100644 --- a/tests/input/genesets/HALLMARK_COAGULATION.yaml +++ b/tests/input/genesets/HALLMARK_COAGULATION.yaml @@ -1,6 +1,6 @@ name: HALLMARK_COAGULATION -gene_symbols: [] -gene_ids: + +gene_symbols: - A2M - ACOX2 - ADAM9 diff --git a/tests/input/genesets/HALLMARK_COMPLEMENT.yaml b/tests/input/genesets/HALLMARK_COMPLEMENT.yaml index 18e3b4fe1..c6292f9f1 100644 --- a/tests/input/genesets/HALLMARK_COMPLEMENT.yaml +++ b/tests/input/genesets/HALLMARK_COMPLEMENT.yaml @@ -1,6 +1,6 @@ name: HALLMARK_COMPLEMENT -gene_symbols: [] -gene_ids: + +gene_symbols: - ACTN2 - ADAM9 - ADRA2B diff --git a/tests/input/genesets/HALLMARK_DNA_REPAIR.yaml b/tests/input/genesets/HALLMARK_DNA_REPAIR.yaml index d558d143e..3b0b7e921 100644 --- a/tests/input/genesets/HALLMARK_DNA_REPAIR.yaml +++ b/tests/input/genesets/HALLMARK_DNA_REPAIR.yaml @@ -1,6 +1,6 @@ name: HALLMARK_DNA_REPAIR -gene_symbols: [] -gene_ids: + +gene_symbols: - AAAS - ADA - ADCY6 diff --git a/tests/input/genesets/HALLMARK_E2F_TARGETS.yaml b/tests/input/genesets/HALLMARK_E2F_TARGETS.yaml index bdc1472c0..e135b5198 100644 --- a/tests/input/genesets/HALLMARK_E2F_TARGETS.yaml +++ b/tests/input/genesets/HALLMARK_E2F_TARGETS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_E2F_TARGETS -gene_symbols: [] -gene_ids: + +gene_symbols: - AK2 - ANP32E - ASF1A diff --git a/tests/input/genesets/HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION.yaml b/tests/input/genesets/HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION.yaml index 7bf299911..9d14a94c4 100644 --- a/tests/input/genesets/HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION.yaml +++ b/tests/input/genesets/HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION.yaml @@ -1,6 +1,6 @@ name: HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION -gene_symbols: [] -gene_ids: + +gene_symbols: - ABI3BP - ACTA2 - ADAM12 diff --git a/tests/input/genesets/HALLMARK_ESTROGEN_RESPONSE_EARLY.yaml b/tests/input/genesets/HALLMARK_ESTROGEN_RESPONSE_EARLY.yaml index c2058fe43..893171e26 100644 --- a/tests/input/genesets/HALLMARK_ESTROGEN_RESPONSE_EARLY.yaml +++ b/tests/input/genesets/HALLMARK_ESTROGEN_RESPONSE_EARLY.yaml @@ -1,6 +1,6 @@ name: HALLMARK_ESTROGEN_RESPONSE_EARLY -gene_symbols: [] -gene_ids: + +gene_symbols: - ABAT - ABCA3 - ABHD2 diff --git a/tests/input/genesets/HALLMARK_ESTROGEN_RESPONSE_LATE.yaml b/tests/input/genesets/HALLMARK_ESTROGEN_RESPONSE_LATE.yaml index 777229a96..558e406f8 100644 --- a/tests/input/genesets/HALLMARK_ESTROGEN_RESPONSE_LATE.yaml +++ b/tests/input/genesets/HALLMARK_ESTROGEN_RESPONSE_LATE.yaml @@ -1,6 +1,6 @@ name: HALLMARK_ESTROGEN_RESPONSE_LATE -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCA3 - ABHD2 - ACOX2 diff --git a/tests/input/genesets/HALLMARK_FATTY_ACID_METABOLISM.yaml b/tests/input/genesets/HALLMARK_FATTY_ACID_METABOLISM.yaml index 0c3d71858..af6b3eb71 100644 --- a/tests/input/genesets/HALLMARK_FATTY_ACID_METABOLISM.yaml +++ b/tests/input/genesets/HALLMARK_FATTY_ACID_METABOLISM.yaml @@ -1,6 +1,6 @@ name: HALLMARK_FATTY_ACID_METABOLISM -gene_symbols: [] -gene_ids: + +gene_symbols: - AADAT - ACAA1 - ACAA2 diff --git a/tests/input/genesets/HALLMARK_G2M_CHECKPOINT.yaml b/tests/input/genesets/HALLMARK_G2M_CHECKPOINT.yaml index 9defaa922..20c65fba7 100644 --- a/tests/input/genesets/HALLMARK_G2M_CHECKPOINT.yaml +++ b/tests/input/genesets/HALLMARK_G2M_CHECKPOINT.yaml @@ -1,6 +1,6 @@ name: HALLMARK_G2M_CHECKPOINT -gene_symbols: [] -gene_ids: + +gene_symbols: - ABL1 - AMD1 - ARID4A diff --git a/tests/input/genesets/HALLMARK_GLYCOLYSIS.yaml b/tests/input/genesets/HALLMARK_GLYCOLYSIS.yaml index b2210d8c9..976618af2 100644 --- a/tests/input/genesets/HALLMARK_GLYCOLYSIS.yaml +++ b/tests/input/genesets/HALLMARK_GLYCOLYSIS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_GLYCOLYSIS -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCB6 - ADORA2B - AGL diff --git a/tests/input/genesets/HALLMARK_HEDGEHOG_SIGNALING.yaml b/tests/input/genesets/HALLMARK_HEDGEHOG_SIGNALING.yaml index 43351c6ea..facc9fae9 100644 --- a/tests/input/genesets/HALLMARK_HEDGEHOG_SIGNALING.yaml +++ b/tests/input/genesets/HALLMARK_HEDGEHOG_SIGNALING.yaml @@ -1,6 +1,6 @@ name: HALLMARK_HEDGEHOG_SIGNALING -gene_symbols: [] -gene_ids: + +gene_symbols: - ACHE - ADGRG1 - AMOT diff --git a/tests/input/genesets/HALLMARK_HEME_METABOLISM.yaml b/tests/input/genesets/HALLMARK_HEME_METABOLISM.yaml index 162ac9897..9df851a0b 100644 --- a/tests/input/genesets/HALLMARK_HEME_METABOLISM.yaml +++ b/tests/input/genesets/HALLMARK_HEME_METABOLISM.yaml @@ -1,6 +1,6 @@ name: HALLMARK_HEME_METABOLISM -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCB6 - ABCG2 - ACKR1 diff --git a/tests/input/genesets/HALLMARK_HYPOXIA.yaml b/tests/input/genesets/HALLMARK_HYPOXIA.yaml index d0282a4b5..181db1ebf 100644 --- a/tests/input/genesets/HALLMARK_HYPOXIA.yaml +++ b/tests/input/genesets/HALLMARK_HYPOXIA.yaml @@ -1,6 +1,6 @@ name: HALLMARK_HYPOXIA -gene_symbols: [] -gene_ids: + +gene_symbols: - ACKR3 - ADM - ADORA2B diff --git a/tests/input/genesets/HALLMARK_IL2_STAT5_SIGNALING.yaml b/tests/input/genesets/HALLMARK_IL2_STAT5_SIGNALING.yaml index ff3cdd1bb..b45e1a76e 100644 --- a/tests/input/genesets/HALLMARK_IL2_STAT5_SIGNALING.yaml +++ b/tests/input/genesets/HALLMARK_IL2_STAT5_SIGNALING.yaml @@ -1,6 +1,6 @@ name: HALLMARK_IL2_STAT5_SIGNALING -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCB1 - ADAM19 - AGER diff --git a/tests/input/genesets/HALLMARK_IL6_JAK_STAT3_SIGNALING.yaml b/tests/input/genesets/HALLMARK_IL6_JAK_STAT3_SIGNALING.yaml index 06cf27dce..bf963c5d8 100644 --- a/tests/input/genesets/HALLMARK_IL6_JAK_STAT3_SIGNALING.yaml +++ b/tests/input/genesets/HALLMARK_IL6_JAK_STAT3_SIGNALING.yaml @@ -1,6 +1,6 @@ name: HALLMARK_IL6_JAK_STAT3_SIGNALING -gene_symbols: [] -gene_ids: + +gene_symbols: - A2M - ACVR1B - ACVRL1 diff --git a/tests/input/genesets/HALLMARK_INFLAMMATORY_RESPONSE.yaml b/tests/input/genesets/HALLMARK_INFLAMMATORY_RESPONSE.yaml index 224b8247b..54ef6a82c 100644 --- a/tests/input/genesets/HALLMARK_INFLAMMATORY_RESPONSE.yaml +++ b/tests/input/genesets/HALLMARK_INFLAMMATORY_RESPONSE.yaml @@ -1,6 +1,6 @@ name: HALLMARK_INFLAMMATORY_RESPONSE -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCA1 - ABI1 - ACVR1B diff --git a/tests/input/genesets/HALLMARK_INTERFERON_ALPHA_RESPONSE.yaml b/tests/input/genesets/HALLMARK_INTERFERON_ALPHA_RESPONSE.yaml index 9101cd7df..7154c6d13 100644 --- a/tests/input/genesets/HALLMARK_INTERFERON_ALPHA_RESPONSE.yaml +++ b/tests/input/genesets/HALLMARK_INTERFERON_ALPHA_RESPONSE.yaml @@ -1,6 +1,6 @@ name: HALLMARK_INTERFERON_ALPHA_RESPONSE -gene_symbols: [] -gene_ids: + +gene_symbols: - ADAR - B2M - BATF2 diff --git a/tests/input/genesets/HALLMARK_INTERFERON_GAMMA_RESPONSE.yaml b/tests/input/genesets/HALLMARK_INTERFERON_GAMMA_RESPONSE.yaml index a5273cdae..1c471ed06 100644 --- a/tests/input/genesets/HALLMARK_INTERFERON_GAMMA_RESPONSE.yaml +++ b/tests/input/genesets/HALLMARK_INTERFERON_GAMMA_RESPONSE.yaml @@ -1,6 +1,6 @@ name: HALLMARK_INTERFERON_GAMMA_RESPONSE -gene_symbols: [] -gene_ids: + +gene_symbols: - ADAR - APOL6 - ARID5B diff --git a/tests/input/genesets/HALLMARK_KRAS_SIGNALING_DN.yaml b/tests/input/genesets/HALLMARK_KRAS_SIGNALING_DN.yaml index e585989d1..21c86c917 100644 --- a/tests/input/genesets/HALLMARK_KRAS_SIGNALING_DN.yaml +++ b/tests/input/genesets/HALLMARK_KRAS_SIGNALING_DN.yaml @@ -1,6 +1,6 @@ name: HALLMARK_KRAS_SIGNALING_DN -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCB11 - ABCG4 - ACTC1 diff --git a/tests/input/genesets/HALLMARK_KRAS_SIGNALING_UP.yaml b/tests/input/genesets/HALLMARK_KRAS_SIGNALING_UP.yaml index 7c9bf42ee..7b859d3ea 100644 --- a/tests/input/genesets/HALLMARK_KRAS_SIGNALING_UP.yaml +++ b/tests/input/genesets/HALLMARK_KRAS_SIGNALING_UP.yaml @@ -1,6 +1,6 @@ name: HALLMARK_KRAS_SIGNALING_UP -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCB1 - ACE - ADAM17 diff --git a/tests/input/genesets/HALLMARK_MITOTIC_SPINDLE.yaml b/tests/input/genesets/HALLMARK_MITOTIC_SPINDLE.yaml index 3ee7f44a7..cd6c2cce1 100644 --- a/tests/input/genesets/HALLMARK_MITOTIC_SPINDLE.yaml +++ b/tests/input/genesets/HALLMARK_MITOTIC_SPINDLE.yaml @@ -1,6 +1,6 @@ name: HALLMARK_MITOTIC_SPINDLE -gene_symbols: [] -gene_ids: + +gene_symbols: - ABI1 - ABL1 - ABR diff --git a/tests/input/genesets/HALLMARK_MTORC1_SIGNALING.yaml b/tests/input/genesets/HALLMARK_MTORC1_SIGNALING.yaml index 0d0cd58d6..df5443ba8 100644 --- a/tests/input/genesets/HALLMARK_MTORC1_SIGNALING.yaml +++ b/tests/input/genesets/HALLMARK_MTORC1_SIGNALING.yaml @@ -1,6 +1,6 @@ name: HALLMARK_MTORC1_SIGNALING -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCF2 - ACACA - ACLY diff --git a/tests/input/genesets/HALLMARK_MYC_TARGETS_V1.yaml b/tests/input/genesets/HALLMARK_MYC_TARGETS_V1.yaml index a3f362331..8dcfe017a 100644 --- a/tests/input/genesets/HALLMARK_MYC_TARGETS_V1.yaml +++ b/tests/input/genesets/HALLMARK_MYC_TARGETS_V1.yaml @@ -1,6 +1,6 @@ name: HALLMARK_MYC_TARGETS_V1 -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCE1 - ACP1 - AIMP2 diff --git a/tests/input/genesets/HALLMARK_MYC_TARGETS_V2.yaml b/tests/input/genesets/HALLMARK_MYC_TARGETS_V2.yaml index 00dc3485a..4458cbc09 100644 --- a/tests/input/genesets/HALLMARK_MYC_TARGETS_V2.yaml +++ b/tests/input/genesets/HALLMARK_MYC_TARGETS_V2.yaml @@ -1,6 +1,6 @@ name: HALLMARK_MYC_TARGETS_V2 -gene_symbols: [] -gene_ids: + +gene_symbols: - AIMP2 - BYSL - CBX3 diff --git a/tests/input/genesets/HALLMARK_MYOGENESIS.yaml b/tests/input/genesets/HALLMARK_MYOGENESIS.yaml index 6fc350bfb..78eb13e96 100644 --- a/tests/input/genesets/HALLMARK_MYOGENESIS.yaml +++ b/tests/input/genesets/HALLMARK_MYOGENESIS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_MYOGENESIS -gene_symbols: [] -gene_ids: + +gene_symbols: - ABLIM1 - ACHE - ACSL1 diff --git a/tests/input/genesets/HALLMARK_NOTCH_SIGNALING.yaml b/tests/input/genesets/HALLMARK_NOTCH_SIGNALING.yaml index b0affc24f..58ef756c2 100644 --- a/tests/input/genesets/HALLMARK_NOTCH_SIGNALING.yaml +++ b/tests/input/genesets/HALLMARK_NOTCH_SIGNALING.yaml @@ -1,6 +1,6 @@ name: HALLMARK_NOTCH_SIGNALING -gene_symbols: [] -gene_ids: + +gene_symbols: - APH1A - ARRB1 - CCND1 diff --git a/tests/input/genesets/HALLMARK_OXIDATIVE_PHOSPHORYLATION.yaml b/tests/input/genesets/HALLMARK_OXIDATIVE_PHOSPHORYLATION.yaml index a768536a7..ca35d470d 100644 --- a/tests/input/genesets/HALLMARK_OXIDATIVE_PHOSPHORYLATION.yaml +++ b/tests/input/genesets/HALLMARK_OXIDATIVE_PHOSPHORYLATION.yaml @@ -1,6 +1,6 @@ name: HALLMARK_OXIDATIVE_PHOSPHORYLATION -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCB7 - ACAA1 - ACAA2 diff --git a/tests/input/genesets/HALLMARK_P53_PATHWAY.yaml b/tests/input/genesets/HALLMARK_P53_PATHWAY.yaml index 87d71b207..d91014a80 100644 --- a/tests/input/genesets/HALLMARK_P53_PATHWAY.yaml +++ b/tests/input/genesets/HALLMARK_P53_PATHWAY.yaml @@ -1,6 +1,6 @@ name: HALLMARK_P53_PATHWAY -gene_symbols: [] -gene_ids: + +gene_symbols: - ABAT - ABCC5 - ABHD4 diff --git a/tests/input/genesets/HALLMARK_PANCREAS_BETA_CELLS.yaml b/tests/input/genesets/HALLMARK_PANCREAS_BETA_CELLS.yaml index e3a7bc6f5..0bf91643a 100644 --- a/tests/input/genesets/HALLMARK_PANCREAS_BETA_CELLS.yaml +++ b/tests/input/genesets/HALLMARK_PANCREAS_BETA_CELLS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_PANCREAS_BETA_CELLS -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCC8 - AKT3 - CHGA diff --git a/tests/input/genesets/HALLMARK_PEROXISOME.yaml b/tests/input/genesets/HALLMARK_PEROXISOME.yaml index a2ec7f9c0..60eb6b9fd 100644 --- a/tests/input/genesets/HALLMARK_PEROXISOME.yaml +++ b/tests/input/genesets/HALLMARK_PEROXISOME.yaml @@ -1,6 +1,6 @@ name: HALLMARK_PEROXISOME -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCB1 - ABCB4 - ABCB9 diff --git a/tests/input/genesets/HALLMARK_PI3K_AKT_MTOR_SIGNALING.yaml b/tests/input/genesets/HALLMARK_PI3K_AKT_MTOR_SIGNALING.yaml index 086fc0dfe..6743a5a3e 100644 --- a/tests/input/genesets/HALLMARK_PI3K_AKT_MTOR_SIGNALING.yaml +++ b/tests/input/genesets/HALLMARK_PI3K_AKT_MTOR_SIGNALING.yaml @@ -1,6 +1,6 @@ name: HALLMARK_PI3K_AKT_MTOR_SIGNALING -gene_symbols: [] -gene_ids: + +gene_symbols: - ACACA - ACTR2 - ACTR3 diff --git a/tests/input/genesets/HALLMARK_PROTEIN_SECRETION.yaml b/tests/input/genesets/HALLMARK_PROTEIN_SECRETION.yaml index b45786e38..108e10872 100644 --- a/tests/input/genesets/HALLMARK_PROTEIN_SECRETION.yaml +++ b/tests/input/genesets/HALLMARK_PROTEIN_SECRETION.yaml @@ -1,6 +1,6 @@ name: HALLMARK_PROTEIN_SECRETION -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCA1 - ADAM10 - ANP32E diff --git a/tests/input/genesets/HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY.yaml b/tests/input/genesets/HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY.yaml index 592270ebf..d221e79fe 100644 --- a/tests/input/genesets/HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY.yaml +++ b/tests/input/genesets/HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY.yaml @@ -1,6 +1,6 @@ name: HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCC1 - ATOX1 - CAT diff --git a/tests/input/genesets/HALLMARK_SPERMATOGENESIS.yaml b/tests/input/genesets/HALLMARK_SPERMATOGENESIS.yaml index 0576c84df..ac3c86dd8 100644 --- a/tests/input/genesets/HALLMARK_SPERMATOGENESIS.yaml +++ b/tests/input/genesets/HALLMARK_SPERMATOGENESIS.yaml @@ -1,6 +1,6 @@ name: HALLMARK_SPERMATOGENESIS -gene_symbols: [] -gene_ids: + +gene_symbols: - ACE - ACRBP - ACRV1 diff --git a/tests/input/genesets/HALLMARK_TGF_BETA_SIGNALING.yaml b/tests/input/genesets/HALLMARK_TGF_BETA_SIGNALING.yaml index fc8aa6789..131b34b9b 100644 --- a/tests/input/genesets/HALLMARK_TGF_BETA_SIGNALING.yaml +++ b/tests/input/genesets/HALLMARK_TGF_BETA_SIGNALING.yaml @@ -1,6 +1,6 @@ name: HALLMARK_TGF_BETA_SIGNALING -gene_symbols: [] -gene_ids: + +gene_symbols: - ACVR1 - APC - ARID4B diff --git a/tests/input/genesets/HALLMARK_TNFA_SIGNALING_VIA_NFKB.yaml b/tests/input/genesets/HALLMARK_TNFA_SIGNALING_VIA_NFKB.yaml index a9c5ed40b..0a18bc700 100644 --- a/tests/input/genesets/HALLMARK_TNFA_SIGNALING_VIA_NFKB.yaml +++ b/tests/input/genesets/HALLMARK_TNFA_SIGNALING_VIA_NFKB.yaml @@ -1,6 +1,6 @@ name: HALLMARK_TNFA_SIGNALING_VIA_NFKB -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCA1 - ACKR3 - AREG diff --git a/tests/input/genesets/HALLMARK_UNFOLDED_PROTEIN_RESPONSE.yaml b/tests/input/genesets/HALLMARK_UNFOLDED_PROTEIN_RESPONSE.yaml index cc8fbcd88..db6b98fc2 100644 --- a/tests/input/genesets/HALLMARK_UNFOLDED_PROTEIN_RESPONSE.yaml +++ b/tests/input/genesets/HALLMARK_UNFOLDED_PROTEIN_RESPONSE.yaml @@ -1,6 +1,6 @@ name: HALLMARK_UNFOLDED_PROTEIN_RESPONSE -gene_symbols: [] -gene_ids: + +gene_symbols: - ALDH18A1 - ARFGAP1 - ASNS diff --git a/tests/input/genesets/HALLMARK_UV_RESPONSE_DN.yaml b/tests/input/genesets/HALLMARK_UV_RESPONSE_DN.yaml index b5066ec30..64c66ba37 100644 --- a/tests/input/genesets/HALLMARK_UV_RESPONSE_DN.yaml +++ b/tests/input/genesets/HALLMARK_UV_RESPONSE_DN.yaml @@ -1,6 +1,6 @@ name: HALLMARK_UV_RESPONSE_DN -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCC1 - ACVR2A - ADD3 diff --git a/tests/input/genesets/HALLMARK_UV_RESPONSE_UP.yaml b/tests/input/genesets/HALLMARK_UV_RESPONSE_UP.yaml index 619268695..540c6c09e 100644 --- a/tests/input/genesets/HALLMARK_UV_RESPONSE_UP.yaml +++ b/tests/input/genesets/HALLMARK_UV_RESPONSE_UP.yaml @@ -1,6 +1,6 @@ name: HALLMARK_UV_RESPONSE_UP -gene_symbols: [] -gene_ids: + +gene_symbols: - ABCB1 - ACAA1 - AGO2 diff --git a/tests/input/genesets/HALLMARK_WNT_BETA_CATENIN_SIGNALING.yaml b/tests/input/genesets/HALLMARK_WNT_BETA_CATENIN_SIGNALING.yaml index 944dda471..1f7fddba4 100644 --- a/tests/input/genesets/HALLMARK_WNT_BETA_CATENIN_SIGNALING.yaml +++ b/tests/input/genesets/HALLMARK_WNT_BETA_CATENIN_SIGNALING.yaml @@ -1,6 +1,6 @@ name: HALLMARK_WNT_BETA_CATENIN_SIGNALING -gene_symbols: [] -gene_ids: + +gene_symbols: - ADAM17 - AXIN1 - AXIN2 diff --git a/tests/input/genesets/HALLMARK_XENOBIOTIC_METABOLISM.yaml b/tests/input/genesets/HALLMARK_XENOBIOTIC_METABOLISM.yaml deleted file mode 100644 index 03d9310bb..000000000 --- a/tests/input/genesets/HALLMARK_XENOBIOTIC_METABOLISM.yaml +++ /dev/null @@ -1,205 +0,0 @@ -name: HALLMARK_XENOBIOTIC_METABOLISM -gene_symbols: [] -gene_ids: - - ABCC2 - - ABCC3 - - ABCD2 - - ABHD6 - - ACO2 - - ACOX1 - - ACOX2 - - ACOX3 - - ACP1 - - ACP2 - - ACSM1 - - ADH1C - - ADH5 - - ADH7 - - AHCY - - AKR1C2 - - AKR1C3 - - ALAS1 - - ALDH2 - - ALDH3A1 - - ALDH9A1 - - ANGPTL3 - - AOX1 - - AP4B1 - - APOE - - AQP9 - - ARG1 - - ARG2 - - ARPP19 - - ASL - - ATOH8 - - ATP2A2 - - BCAR1 - - BCAT1 - - BLVRB - - BPHL - - CA2 - - CASP6 - - CAT - - CBR1 - - CCL25 - - CD36 - - CDA - - CDO1 - - CES1 - - CFB - - CNDP2 - - COMT - - CROT - - CRP - - CSAD - - CYB5A - - CYFIP2 - - CYP17A1 - - CYP1A1 - - CYP1A2 - - CYP26A1 - - CYP27A1 - - CYP2C18 - - CYP2E1 - - CYP2J2 - - CYP2S1 - - CYP4F2 - - DCXR - - DDAH2 - - DDC - - DDT - - DHPS - - DHRS1 - - DHRS7 - - ECH1 - - ELOVL5 - - ENPEP - - ENTPD5 - - EPHA2 - - EPHX1 - - ESR1 - - ETFDH - - ETS2 - - F10 - - F11 - - FABP1 - - FAH - - FAS - - FBLN1 - - FBP1 - - FETUB - - FMO1 - - FMO3 - - G6PC1 - - GABARAPL1 - - GAD1 - - GART - - GCH1 - - GCKR - - GCLC - - GCNT2 - - GNMT - - GSR - - GSS - - GSTA3 - - GSTM4 - - GSTO1 - - GSTT2 - - HACL1 - - HES6 - - HGFAC - - HMOX1 - - HNF4A - - HPRT1 - - HRG - - HSD11B1 - - HSD17B2 - - ID2 - - IDH1 - - IGF1 - - IGFBP1 - - IGFBP4 - - IL1R1 - - IRF8 - - ITIH1 - - ITIH4 - - JUP - - KARS1 - - KYNU - - LCAT - - LEAP2 - - LONP1 - - LPIN2 - - MAN1A1 - - MAOA - - MARCHF6 - - MBL2 - - MCCC2 - - MPP2 - - MT2A - - MTHFD1 - - NDRG2 - - NFS1 - - NINJ1 - - NMT1 - - NPC1 - - NQO1 - - PAPSS2 - - PC - - PDK4 - - PDLIM5 - - PEMT - - PGD - - PGRMC1 - - PINK1 - - PLG - - PMM1 - - POR - - PPARD - - PROS1 - - PSMB10 - - PTGDS - - PTGES - - PTGES3 - - PTGR1 - - PTS - - PYCR1 - - RAP1GAP - - RBP4 - - REG1A - - RETSAT - - SAR1B - - SERPINA6 - - SERPINE1 - - SERTAD1 - - SHMT2 - - SLC12A4 - - SLC1A5 - - SLC22A1 - - SLC35B1 - - SLC35D1 - - SLC46A3 - - SLC6A12 - - SLC6A6 - - SMOX - - SPINT2 - - SSR3 - - TAT - - TDO2 - - TGFB2 - - TKFC - - TMBIM6 - - TMEM176B - - TMEM97 - - TNFRSF1A - - TPST1 - - TTPA - - TYR - - UGDH - - UPB1 - - UPP1 - - VNN1 - - VTN - - XDH -taxon: human -description: null \ No newline at end of file