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<p><font style="font-weight: bold; color: rgb(0, 102, 0);" size="+3">Deep Learning for Biomedical Discovery and Data Mining</font></p><p><font style="color: rgb(0, 102, 0);" size="+3">A tutorial @PAKDD18, Melbourne, June 2018.</font></p><p style="color: black; font-weight: bold;"><font size="+2">Slides (<a href="talks/pakdd18-tute-part1.pdf">Part I</a>; <a href="talks/pakdd18-tute-part2.pdf">Part II</a>)</font></p><p style="color: black; font-weight: bold;"><font size="+2">Abstract: <span style="font-weight: normal;">The
goals of this tutorial are to provide the general PAKDD audience with
knowledge and materials about a great venture for KDD research – the
intersection between deep learning and biomedicine and to provide the
deep learning community with relatively new, high impact research
problems within biomedicine.</span><span style="font-weight: normal;">
The tutorial introduces the state of the field for deep learning, and
argues how biomedicine is an ideal data–intensive domain. It gives a
brief review of deep learning, covering classic neural architectures
including feedforward, recurrent and convolutional nets and more
advanced topics including CapsNet, powerful memory-augmented neural
nets (MANN), as well as models for graph data. </span><span style="font-weight: normal;">Two
major subtopics of Genomics are covered: nanopore sequencing (which is
about converting electrical signals into DNA character sequences), and
genomics modeling (which is about making sense of the DNA sequences
for multiple biological processes).</span><span style="font-weight: normal;"> </span><span style="font-weight: normal;">For
healthcare coverage is on data mining of Electronic Medical Records.
Two main problems are considered: The first is modeling
time-series and the second is mid-term health trajectories
prediction.</span><span style="font-weight: normal;"> Then I will cover recent advances in data eficient methods: few-shot learning
and deep generative models (RBM, VAE and GAN). This describes how
to apply these advances to drug designs, and the future outlook into a
5-year horizon and beyond on the joint venture of deep learning and
biomedicine.</span><br style="font-weight: normal;"><span style="font-weight: normal;"><span style="font-weight: bold;"></span></span></font></p><p style="color: black; font-weight: bold;"><font size="+2"><span style="font-weight: normal;"><span style="font-weight: bold;">Prerequisite</span>: <span style="font-style: italic;">the
tutorial does not require detailed prior knowledge of biomedicine or
deep learning, but basic familiarity with machine learning is assumed.</span></span></font></p><p style="color: black; font-weight: bold;"><font size="+2">Outline:</font></p><p style="color: black; font-weight: bold; margin-left: 40px;"><font size="+2">Part I</font></p><p style="color: black; font-weight: bold; margin-left: 40px;"><font size="+2"><span style="font-weight: normal;">Topic 1: Introduction (20 mins)</span><br style="font-weight: normal;"><span style="font-weight: normal;">Topic 2: Brief review of deep learning (30 mins)</span><br style="font-weight: normal;"></font></p><ul style="margin-left: 40px;"><li><font size="+2"><span style="font-weight: normal;">Classic architectures</span></font></li><li><font size="+2"><span style="font-weight: normal;">Capsules & graphs</span></font></li><li><font size="+2"><span style="font-weight: normal;">Memory & attention</span></font></li></ul><p style="color: black; font-weight: bold; margin-left: 40px;"><font size="+2"><span style="font-weight: normal;">Topic 3: Genomics (30 mins)</span><br style="font-weight: normal;"></font></p><ul style="margin-left: 40px;"><li><font size="+2"><span style="font-weight: normal;">Nanopore sequencing</span></font></li><li><font size="+2"><span style="font-weight: normal;">Genomics modelling</span></font></li></ul><p style="color: black; font-weight: bold; margin-left: 40px;"><font size="+2"><span style="font-weight: normal;">QA (10 mins)</span></font></p><p style="color: black; font-weight: bold; margin-left: 40px;"><font size="+2">Part II</font><br style="font-weight: normal;"><br style="font-weight: normal;"><font size="+2"><span style="font-weight: normal;">Topic 4: Healthcare (40 mins)</span></font><br style="font-weight: normal;"></p><ul style="margin-left: 40px;"><li><font size="+2"><span style="font-weight: normal;">Time series (regular & irregular)</span></font></li><li><font size="+2"><span style="font-weight: normal;">EMR analysis: Trajectories prediction</span></font></li><li><font size="+2"><span style="font-weight: normal;">EMR analysis: Sequence generation</span></font></li></ul><p style="color: black; font-weight: bold; margin-left: 40px;"><font size="+2"><span style="font-weight: normal;">Topic 5: Data efficiency methods (40 mins)</span></font><br style="font-weight: normal;"></p><ul style="margin-left: 40px;"><li><font size="+2"><span style="font-weight: normal;">Few-shot learning</span></font></li><li><font size="+2"><span style="font-weight: normal;">Generative models</span></font></li><li><font size="+2"><span style="font-weight: normal;">Unsupervised learning of drugs</span></font></li></ul><p style="color: black; font-weight: bold; margin-left: 40px;"><font size="+2"><span style="font-weight: normal;">Topic 6: Future outlook</span></font></p><p style="color: black; font-weight: bold; margin-left: 40px;"><font size="+2"><span style="font-weight: normal;">QA (10 mins)</span></font></p><ul style="color: black;">
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<p align="justify"><font style="font-weight: bold;" size="5">References</font></p><ul>
<li><a href="#Genomics__drug_design"><font size="+2">Genomics & drug design</font></a></li><li><a href="#Healthcare"><font size="+2">Healthcare</font></a></li>
<li><a href="#Deep_learning_fundamentals"><font size="+2">Deep learning fundamentals</font></a></li>
</ul><font size="+2"><span style="font-weight: bold; font-family: Times New Roman,Times,serif;"><br><a name="Genomics__drug_design"></a><span style="font-family: Times New Roman,Times,serif;"></span></span></font><font style="font-weight: bold;" size="5">Genomics & drug design</font><font size="+2"><span style="font-weight: bold; font-family: Times New Roman,Times,serif;"><span style="font-family: Times New Roman,Times,serif;"></span><br></span></font><br>
<ol>
<li><font size="+2">Altae-Tran, Han, et al. "Low Data Drug Discovery with One-Shot Learning." <span style="font-style: italic;">ACS central science</span> 3.4 (2017): 283-293.</font></li><li><font size="+2">Alipanahi, Babak, et al. "Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning."<span style="font-style: italic;"> Nature biotechnology</span> 33.8 (2015): 831-838.</font></li>
<li><font size="+2">Angermueller, Christof, et al. "Deep learning for computational biology." <span style="font-style: italic;">Molecular systems biology</span> 12.7 (2016): 878.<br>
</font></li>
<li><font size="+2">Boža,
Vladimír, Broňa Brejová, and Tomáš Vinař. "DeepNano: Deep recurrent
neural networks for base calling in MinION nanopore reads." <span style="font-style: italic;">PloS one</span> 12.6 (2017): e0178751.</font></li>
<li><font size="+2">Ching, Travers, et al. "Opportunities And Obstacles For Deep Learning In Biology And Medicine." <span style="font-style: italic;">bioRxiv</span> (2018): 142760.</font></li>
<li><font size="+2">Duvenaud, David K., et al. "Convolutional networks on graphs for learning molecular fingerprints." <span style="font-style: italic;">Advances in neural information processing systems</span>. 2015.</font></li>
<li><font size="+2">Eser,
Umut, and L. Stirling Churchman. "FIDDLE: An integrative deep learning
framework for functional genomic data inference." <span style="font-style: italic;">bioRxiv</span> (2016): 081380.<br>
</font></li>
<li><font size="+2">Gilmer, Justin, et al. "Neural message passing for quantum chemistry."<span style="font-style: italic;"> arXiv preprint arXiv:1704.01212</span> (2017).</font></li>
<li><font size="+2">Gómez-Bombarelli, Rafael, et al. "Automatic chemical design using a data-driven continuous representation of molecules." <span style="font-style: italic;">ACS Central Science</span> (2016)</font></li>
<li><font size="+2">Gupta, Anvita, et al. "Generative Recurrent Networks for De Novo Drug Design." <span style="font-style: italic;">Molecular Informatics</span> (2017).</font></li>
<li><font size="+2">Jin, W., Barzilay, R., & Jaakkola, T. (2018). "Junction Tree Variational Autoencoder for Molecular Graph Generation". <span style="font-style: italic;">ICML'18</span>.</font></li>
<li><font size="+2">Kadurin, A., Aliper, A., Kazennov, A.,
Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A.
(2017). "The cornucopia of meaningful leads: Applying deep adversarial
autoencoders for new molecule development in oncology". <span style="font-style: italic;">Oncotarget</span>, 8(7), 10883.</font></li>
<li><font size="+2">Kadurin, A., Nikolenko, S., Khrabrov, K.,
Aliper, A., & Zhavoronkov, A. (2017). "druGAN: an advanced
generative adversarial autoencoder model for de novo generation of new
molecules with desired molecular properties in silico". <span style="font-style: italic;">Molecular pharmaceutics</span>, 14(9), 3098-3104.</font></li>
<li><font size="+2">Kien Do, et al. "Attentional Multilabel Learning over Graphs-A message passing approach." <span style="font-style: italic;">arXiv preprint </span>arXiv:1804.00293(2018).<br>
</font></li>
<li><font size="+2">Kusner, Matt J., Brooks Paige, and José Miguel Hernández-Lobato. "Grammar Variational Autoencoder." <span style="font-style: italic;">arXiv preprint arXiv:1703.01925</span> (2017).</font></li>
<li><font size="+2">Lanchantin, Jack, Ritambhara Singh, and Yanjun Qi. "Memory Matching Networks for Genomic Sequence Classification." <span style="font-style: italic;">arXiv preprint arXiv:1702.06760</span> (2017).</font></li>
<li><font size="+2">Leung, Michael KK, et al. "Deep learning of the tissue-regulated splicing code." <span style="font-style: italic;">Bioinformatics</span> 30.12 (2014): i121-i129.<br>
</font></li>
<li><font size="+2">Olivecrona, Marcus, et al. "Molecular De Novo Design through Deep Reinforcement Learning." <span style="font-style: italic;">arXiv preprint arXiv:1704.07555</span>(2017).</font></li>
<li><font size="+2">Penmatsa,
Aravind, Kevin H. Wang, and Eric Gouaux. "X-ray structure of dopamine
transporter elucidates antidepressant mechanism." <span style="font-style: italic;">Nature</span> 503.7474 (2013): 85-90.</font></li>
<li><font size="+2">Pham, Trang et al. "Graph Classification via Deep Learning with Virtual Nodes. <span style="font-style: italic;">Third Representation Learning for Graphs Workshop (ReLiG 2017)</span>.</font></li>
<li><font size="+2">Pham, Trang, Truyen Tran, and Svetha Venkatesh. "Graph Memory Networks for Molecular Activity Prediction." <span style="font-style: italic;">ICPR'18</span>.<br>
</font></li>
<li><font size="+2">Quang, Daniel, and Xiaohui Xie. "DanQ: a
hybrid convolutional and recurrent deep neural network for quantifying
the function of DNA sequences." <span style="font-style: italic;">Nucleic acids research</span> 44.11 (2016): e107-e107.<br>
</font></li>
<li><font size="+2">Romero, Adriana, et al. "Diet Networks: Thin Parameters for Fat Genomic." <span style="font-style: italic;">arXiv preprint arXiv:1611.09340</span> (2016).</font></li>
<li><font size="+2">Roses, Allen D. "Pharmacogenetics in drug discovery and development: a translational perspective." <span style="font-style: italic;">Nature reviews Drug discovery</span> 7.10 (2008): 807-817.</font></li><li><font size="+2">Segler, Marwin HS, et al. "Generating focussed molecule libraries for drug discovery with recurrent neural networks." <span style="font-style: italic;">arXiv preprint arXiv:1701.01329</span> (2017). </font></li><li><font size="+2">Segler,
Marwin, Mike Preuß, and Mark P. Waller. "Towards" AlphaChem": Chemical
Synthesis Planning with Tree Search and Deep Neural Network Policies." <span style="font-style: italic;">arXiv preprint arXiv:1702.00020</span>(2017).</font></li>
<li><font size="+2">Simonovsky,
M., & Komodakis, N. (2018). "GraphVAE: Towards Generation of Small
Graphs Using Variational Autoencoders". <span style="font-style: italic;">arXiv preprint</span> arXiv:1802.03480.<br>
</font></li>
<li><font size="+2">Stoiber, Marcus, and James Brown. "BasecRAWller: Streaming Nanopore Basecalling Directly from Raw Signal." <span style="font-style: italic;">bioRxiv (2017)</span>: 133058.</font></li>
<li><font size="+2">Teng, Haotien, et al. "Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning, <span style="font-style: italic;">GigaScience</span>, Volume 7, Issue 5, 1 May 2018, giy037. <br>
</font></li>
</ol>
<font size="+2"><span style="font-weight: bold; font-family: Times New Roman,Times,serif;"></span></font><font size="+2"><span style="font-weight: bold; font-family: Times New Roman,Times,serif;"><a name="Healthcare"></a></span></font><font style="font-weight: bold;" size="5">Healthcare</font><font size="+2"><span style="font-weight: bold; font-family: Times New Roman,Times,serif;"><br></span></font>
<ol>
<li><font size="+2">Acharya,
U. Rajendra, et al. "Application of deep convolutional neural network
for automated detection of myocardial infarction using ECG
signals." <span style="font-style: italic;">Information Sciences</span> 415 (2017): 190-198<br>
</font></li>
<li><font size="+2">Che, Zhengping, et al. "Recurrent neural networks for multivariate time series with missing values." <span style="font-style: italic;">arXiv preprint arXiv:1606.01865</span>(2016).</font></li>
<li><font size="+2">Choi, Edward, et al. "Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks." <span style="font-style: italic;">arXiv preprint arXiv:1703.06490</span> (2017).</font></li>
<li><font size="+2">Choi, Edward, et al. "Doctor AI: Predicting clinical events via recurrent neural networks." <span style="font-style: italic;">Machine Learning for Healthcare Conference</span>. 2016.</font></li>
<li><font size="+2">Choi, Edward, et al. "GRAM: Graph-based attention model for healthcare representation learning." <span style="font-style: italic;">Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</span>. ACM, 2017.</font></li>
<li><font size="+2">Choi, Edward, et al. "RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism." <span style="font-style: italic;">Advances in Neural Information Processing Systems</span>. 2016.</font></li>
<li><font size="+2">Do, Kien et al. "Learning Recurrent Matrix Representation", <span style="font-style: italic;">Third Representation Learning for Graphs Workshop (ReLiG 2017)</span>, also: <span style="font-style: italic;">arXiv preprint arXiv: 1703.01454</span>.</font></li>
<li><font size="+2">Esteva, Andre, et al. "Dermatologist-level classification of skin cancer with deep neural networks." <span style="font-style: italic;">Nature</span> 542.7639 (2017): 115-118.</font></li>
<li><font size="+2">Hung Le, Truyen Tran, and Svetha Venkatesh. “Dual Control Memory Augmented Neural Networks for Treatment Recommendations”, <span style="font-style: italic;">PAKDD'18</span>. <br>
</font></li>
<li><font size="+2">Hung Le, Truyen Tran, and Svetha Venkatesh. "Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning." <span style="font-style: italic;">KDD'18</span>.<br>
</font></li>
<li><font size="+2">Lipton, Zachary C., et al. "Learning to diagnose with LSTM recurrent neural networks."<span style="font-style: italic;"> arXiv preprint arXiv:1511.03677</span>(2015).</font></li>
<li><font size="+2">Miotto,
Riccardo, et al. "Deep patient: An unsupervised representation to
predict the future of patients from the electronic health records." <span style="font-style: italic;">Scientific reports</span> 6 (2016): 26094.</font></li><li><font size="+2">Nguyen, Phuoc. "Deep Learning to Attend to Risk in ICU", <span style="font-style: italic;"> IJCAI'17 Workshop on Knowledge Discovery in Healthcare II: Towards Learning Healthcare Systems</span> (KDH 2017). </font></li><li><font size="+2">Nguyen, Phuoc et al. "Deepr: A Convolutional Net for Medical Records". <span style="font-style: italic;">IEEE Journal of Biomedical and Health Informatics</span>, vol. 21, no. 1, pp. 22–30, Jan. 2017, Doi: 10.1109/JBHI.2016.2633963</font></li><li><font size="+2">Phuoc
Nguyen, Truyen Tran, and Svetha Venkatesh. "Resset: A Recurrent Model
for Sequence of Sets with Applications to Electronic Medical
Records." <span style="font-style: italic;">IJCNN</span> (2018).<br>
</font></li>
<li><font size="+2">Nguyen, Tu et al. "Tensor-variate Restricted Boltzmann Machines", <span style="font-style: italic;">AAAI</span> 2015.</font></li>
<li><font size="+2">Pham, Trang et al. "Predicting
healthcare trajectories from medical records: A deep learning approach". <span style="font-style: italic;">Journal of Biomedical Informatics</span>, April 2017, DOI: 10.1016/j.jbi.2017.04.001.</font></li>
<li><font size="+2">Tran, Truyen. "Living in the future: AI for healthcare". <span style="font-style: italic;">Blog</span>, Feb 2017.</font></li>
<li><font size="+2">Zhang et al., “Leap: Learning to prescribe effective and safe treatment combinations for multimorbidity”, <span style="font-style: italic;">KDD'17</span>. <br>
</font></li>
</ol>
<font size="+2"><span style="font-weight: bold; font-family: Times New Roman,Times,serif;"></span><span style="font-weight: bold; font-family: Times New Roman,Times,serif;"><a name="Deep_learning_fundamentals"></a></span></font><font style="font-weight: bold;" size="5">Deep learning fundamentals</font><font size="+2"><span style="font-weight: bold; font-family: Times New Roman,Times,serif;"></span><br style="font-family: Times New Roman,Times,serif;"></font><ol>
<li><font size="+2">Goodfellow, Ian et al., "Generative Adversarial Nets". <span style="font-style: italic;">NIPS</span>, 2014.</font></li>
<li><font size="+2">Graves, Alex et al. "Hybrid
computing using a neural network with dynamic external memory", <span style="font-style: italic;">Nature</span>, 2016.</font></li>
<li><font size="+2">Hochreiter, Sepp, et al. "Learning to learn using gradient descent". In <span style="font-style: italic;">Artificial Neural Networks (ICANN</span>) 2001, pp. 87–94. Springer,2001</font></li>
<li><font size="+2">Kingma, Diederik P., and Max Welling. "Auto-encoding variational Bayes."<span style="font-style: italic;"> arXiv preprint</span> arXiv:1312.6114 (2013).</font></li>
<li><font size="+2">Koch, Gregory et al. "Siamese neural networks for one-shot image recognition." <span style="font-style: italic;">ICML Deep Learning Workshop</span>. Vol. 2. 2015.</font></li>
<li><font size="+2">Kumar, Ankit, et al. "Ask me anything: Dynamic memory networks for natural language processing." <span style="font-style: italic;">International Conference on Machine Learning</span>. 2016.</font></li>
<li><font size="+2">Mishra, Nikhil, et al. "Meta-Learning with Temporal Convolutions." <span style="font-style: italic;">arXiv preprint arXiv:1707.03141</span> (2017).</font></li><li><font size="+2">Santoro, Adam, et al. "Meta-learning with memory-augmented neural networks."<span style="font-style: italic;"> International conference on machine learning</span>, 2016</font></li><li><font size="+2">Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "End-to-end memory networks." <span style="font-style: italic;">Advances in neural information processing systems</span>. 2015.</font></li>
<li><font size="+2">Wagstaff, K. L. (2012, June). "Machine learning that matters". In <span style="font-style: italic;">Proceedings of the 29th International Coference on International Conference on Machine Learning</span> (pp. 1851-1856). Omnipress.<br>
</font></li>
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