(Source: TEKsystems)







Deep learning for biomedicine

A tutorial @ACML17, Seoul, Nov 2017.

Slides (Part I; Part II)


Genomics & drug design

  1. Altae-Tran, Han, et al. "Low Data Drug Discovery with One-Shot Learning." ACS central science 3.4 (2017): 283-293.
  2. Alipanahi, Babak, et al. "Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning." Nature biotechnology 33.8 (2015): 831-838.
  3. Boža, Vladimír, Broňa Brejová, and Tomáš Vinař. "DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads." PloS one 12.6 (2017): e0178751.
  4. Ching, Travers, et al. "Opportunities And Obstacles For Deep Learning In Biology And Medicine." bioRxiv (2017): 142760.
  5. Duvenaud, David K., et al. "Convolutional networks on graphs for learning molecular fingerprints." Advances in neural information processing systems. 2015.
  6. Gilmer, Justin, et al. "Neural message passing for quantum chemistry." arXiv preprint arXiv:1704.01212 (2017).
  7. Gómez-Bombarelli, Rafael, et al. "Automatic chemical design using a data-driven continuous representation of molecules." arXiv preprint arXiv:1610.02415 (2016)
  8. Gupta, Anvita, et al. "Generative Recurrent Networks for De Novo Drug Design." Molecular Informatics (2017).
  9. Kusner, Matt J., Brooks Paige, and José Miguel Hernández-Lobato. "Grammar Variational Autoencoder." arXiv preprint arXiv:1703.01925 (2017).
  10. Lanchantin, Jack, Ritambhara Singh, and Yanjun Qi. "Memory Matching Networks for Genomic Sequence Classification." arXiv preprint arXiv:1702.06760 (2017).
  11. Olivecrona, Marcus, et al. "Molecular De Novo Design through Deep Reinforcement Learning." arXiv preprint arXiv:1704.07555(2017).
  12. Penmatsa, Aravind, Kevin H. Wang, and Eric Gouaux. "X-ray structure of dopamine transporter elucidates antidepressant mechanism." Nature 503.7474 (2013): 85-90.
  13.  Pham, Trang et al. "Graph Classification via Deep Learning with Virtual Nodes. Third Representation Learning for Graphs Workshop (ReLiG 2017).
  14. Romero, Adriana, et al. "Diet Networks: Thin Parameters for Fat Genomic." arXiv preprint arXiv:1611.09340 (2016).
  15. Roses, Allen D. "Pharmacogenetics in drug discovery and development: a translational perspective." Nature reviews Drug discovery 7.10 (2008): 807-817.
  16. Segler, Marwin HS, et al. "Generating focussed molecule libraries for drug discovery with recurrent neural networks." arXiv preprint arXiv:1701.01329 (2017). 
  17. Segler, Marwin, Mike Preuß, and Mark P. Waller. "Towards" AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies." arXiv preprint arXiv:1702.00020(2017).
  18. Stoiber, Marcus, and James Brown. "BasecRAWller: Streaming Nanopore Basecalling Directly from Raw Signal." bioRxiv (2017): 133058.
  19. Teng, Haotien, et al. "Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning." bioRxiv(2017): 179531. 
Biomedical imaging
  1. Kraus, Oren Z., and Brendan J. Frey. "Computer vision for high content screening." Critical reviews in biochemistry and molecular biology 51.2 (2016): 102-109.
  2. Litjens, Geert, et al. "A survey on deep learning in medical image analysis." arXiv preprint arXiv:1702.05747 (2017).
  3. Quinn, John A., et al. "Deep convolutional neural networks for microscopy-based point of care diagnostics." Machine Learning for Healthcare Conference. 2016.

  1. Che, Zhengping, et al. "Recurrent neural networks for multivariate time series with missing values." arXiv preprint arXiv:1606.01865(2016).
  2. Choi, Edward, et al. "Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks." arXiv preprint arXiv:1703.06490 (2017).
  3. Choi, Edward, et al. "Doctor AI: Predicting clinical events via recurrent neural networks." Machine Learning for Healthcare Conference. 2016.
  4. Choi, Edward, et al. "GRAM: Graph-based attention model for healthcare representation learning." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017.
  5. Choi, Edward, et al. "RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism." Advances in Neural Information Processing Systems. 2016.
  6. Do, Kien et al. "Learning Recurrent Matrix Representation", Third Representation Learning for Graphs Workshop (ReLiG 2017), also: arXiv preprint arXiv: 1703.01454.
  7. Esteva, Andre, et al. "Dermatologist-level classification of skin cancer with deep neural networks." Nature 542.7639 (2017): 115-118.
  8. Lipton, Zachary C., et al. "Learning to diagnose with LSTM recurrent neural networks." arXiv preprint arXiv:1511.03677(2015).
  9. Miotto, Riccardo, et al. "Deep patient: An unsupervised representation to predict the future of patients from the electronic health records." Scientific reports 6 (2016): 26094.
  10. Nguyen, Phuoc.  "Deep Learning to Attend to Risk in ICU",  IJCAI'17 Workshop on Knowledge Discovery in Healthcare II: Towards Learning Healthcare Systems (KDH 2017).
  11. Nguyen, Phuoc et al.  "Deepr: A Convolutional Net for Medical Records". IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 22–30, Jan. 2017, Doi: 10.1109/JBHI.2016.2633963
  12. Nguyen,  Tu et al.  "Tensor-variate Restricted Boltzmann Machines", AAAI 2015.
  13. Pham, Trang et al. "Predicting healthcare trajectories from medical records: A deep learning approach". Journal of Biomedical Informatics, April 2017, DOI: 10.1016/j.jbi.2017.04.001.
  14. Tran, Truyen. "Living in the future: AI for healthcare". Blog, Feb 2017.
Deep learning fundamentals
  1. Goodfellow, Ian et al., "Generative Adversarial Nets". NIPS, 2014.
  2. Graves, Alex et al. "Hybrid computing using a neural network with dynamic external memory", Nature, 2016.
  3. Hochreiter, Sepp, et al. "Learning to learn using gradient descent". In Artificial Neural Networks (ICANN) 2001, pp. 87–94. Springer,2001
  4. Kingma, Diederik P., and Max Welling. "Auto-encoding variational Bayes." arXiv preprint arXiv:1312.6114 (2013).
  5. Koch, Gregory et al. "Siamese neural networks for one-shot image recognition." ICML Deep Learning Workshop. Vol. 2. 2015.
  6. Kumar, Ankit, et al. "Ask me anything: Dynamic memory networks for natural language processing." International Conference on Machine Learning. 2016.
  7. Mishra, Nikhil, et al. "Meta-Learning with Temporal Convolutions." arXiv preprint arXiv:1707.03141 (2017).
  8. Santoro, Adam, et al. "Meta-learning with memory-augmented neural networks." International conference on machine learning, 2016
  9. Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "End-to-end memory networks." Advances in neural information processing systems. 2015.