A repository to explore the use of ontology-based graphs (generated with MOWL) as background knowledge for scientific articles similarity.
- RELISH corpus
- RELISH corpus pre-annotated with terms from the MeSH vocabulary provided by ZB MED
- MOWL library to generate graphs from ontologies
- Libraries to create graph embeddings
- Using MOWL to generate a graph corresponding to the MeSH vocabulary. The input for this step is the MeSH vocabulary and the output is a graph.
- Using the RELISH corpus pre-annotated with MeSH terms (i.e., any entity from the MeSH vocabulary recognized in the text will have been replaced by the corresponding term ID) create a mini-graph for each document. Use co-occurrence to add weights to the edges. This graph will be most likely kind of disconnected (e.g., not too many edges). The input for this step will be a TSV file where each row is a pre-annotated article from the RELISH corpus (see example below) while the output will be a graph per document.
- For all nodes in a document mini-graph, identify the direct nodes coming from the MeSH-based graph and add them to the mini-graph. This will give you a more connected graph, with background knowledge coming from the MeSH vocabulary. The inputs for this step are the document mini-graphs while the outputs will be document enriched mini-graphs.
- Create graph embeddings for the documents in the RELISH corpus.
- Calculate the cosine similarity for each existing pair of documents in the corpus.
- Analyze correlation of the cosine similarities using this approach versus another one created in the OntoClue framework.
- Discuss results (lessons learnt, possible variations, limitations).
| PMID | title | abstract |
|---|---|---|
| 10821866 | meshd003643 signal-induced localization of p53 meshd011506 to meshd008928 a potential meshd012380 in apoptotic signaling | the mechanism of p53-mediated meshd017209 after cellular stress remains poorly understood evidence suggests that p53 induces meshd016923 by a multitude of molecular pathways involving activation of target meshd005796 and transcriptionally independent direct signaling meshd008928 meshd010988 a key meshd012380 in meshd017209 we show here that a fraction of p53 meshd011506 localizes to meshd008928 at the onset of p53-dependent meshd017209 but not during p53-independent meshd017209 or p53-mediated meshd059447 the accumulation of p53 to meshd008928 is rapid within 1 h after p53 activation and precedes changes in meshd053078 meshd045304 release and meshd053148 activation meshd016253 and immuno-meshd005453-activated meshd002477 sorter meshq000032 of isolated meshd008928 show that the majority of mitochondrial p53 localizes to the membranous compartment whereas a fraction is found in a complex with the mitochondrial import motor mt hsp70 after induction of ectopic p53 without additional meshd004249 in p53-deficient meshd002477 p53 again partially localizes to meshd008928 preceding the onset of meshd017209 overexpression of anti-apoptotic bcl-2 or bcl-xl abrogates stress signal-mediated mitochondrial p53 accumulation and meshd017209 but not meshd059447 suggesting a meshd005246 signaling loop between p53 and mitochondrial apoptotic regulators importantly bypassing the nucleus by targeting p53 to meshd008928 using import leader fusions is sufficient to induce meshd017209 in p53-deficient meshd002477 we propose a model where p53 can contribute to meshd017209 by direct signaling at the meshd008928 thereby amplifying the transcription-dependent meshd017209 of p53 |
- We are not capturing relations from the text between two recognized.
- Some biologically relevant entities, see P53 in the example, are not part of the MeSH vocabulary (however this approach is for a recommendation system and not for a specific biological task so might still be ok).
- Explore Named Entity Recognition approaches other than simple/direct lexical matching.
- Using sentiment analysis to add positive/negative connotation to the relation betweeen two entities recognized in the text.
- Nelson David Quiñones Virgen, co-lead, orcid:0000-0002-5037-0443
- Leyla Jael Castro, co-lead, orcid:0000-0003-3986-0510
- Asmaa Alqhtani, participant, orcid:0000-0002-3119-5109
- Rahaf Alayed, participant, orcid:
- Layan Aljohani, participant, orcid:0000-0001-6954-2973
- Maria G Gomez Castillo, participant, orcid:
- Day 1: Formation of the team, discussion on the approach, distribution of initial tasks.
- Task 1: Asmaa, Rahaf, Layan, Maria. Getting a graph out of MeSH using mOWL
- Task 2: Nelson. Explore strategies to create mini-graphs representing the publications
- Day 2
- Task 1 done, using the KAUST cloud, output saved in a pkl file, the projection used was DL2Vec for taxonomy only in two directions.
- Task 2: MeSH terms extracted to a JSON file using the PMID as key for each article, invlufinh the co-occurrence count.
- Task 3: Maria and Rahaf. From the MeSH graph created in Task 1, use a graph embedding algorithm to obtain the graph embeddings, each concept will be assigned a vector
- Task 4a: Layan and Maria. Define and implement a strategy to get all the concept embeddings for a document and move to document embeddings. Once the MeSH graph embeddings are ready, apply to the actual data.
- Task 4b: Nelson. Define and implement a strategy to create mini-graphs out the concepts identified in each document and the MeSH graph embeddings.
- Day 3
- Leyla Jael. Add information to the mid-term report. Done
- Task 4a. Asma, Rahaf, Maria, Layan. Word embeddings for the graph are done. Continuing with the document embeddings for RELISH corpus.
- Task 4b. Nelson. Exploring options based on lowest common ancestor.
This project started as part of the BioHackathon MENA 2023