CS 260, Winter 2018: Deep Learning for Computational Biology

Class Meeting

MWF, 3:10pm - 4pm, WCH 139

Office hours

By appointment, please email me.


None, some knowledge of Machine Learning and Computational Biology


  • Slides/1 [PDF Format 2slides/page] (Intro)
  • Slides/2 [PDF Format 2slides/page] (Intro)
  • List of application domains

  • Transcription (DNA Binding, RNA Binding, Enhancers/Promoters, Alternative Splicing, MicroRNA post-transcriptional regulation)
  • Epigenetics (DNA Methylation, Nucleosomes, Histones Tail Modifications, Chromatin)
  • Proteins (3D structure prediction, secondary structure prediction, docking)
  • Structural variations and SNPs
  • Course Format

    The course is structured it as a "journal club", where students alternate presenting papers and highlight and discuss possible new line of research.

    Calendar of Presentations

  • Jan  8: Cancelled
  • Jan 10: Cancelled
  • Jan 12: Stefano: Course Organization, Presentation of the application domains
  • Jan 15: HOLIDAY
  • Jan 17: Stefano: Presentation of the application domains
  • Jan 19: Stefano: Presentation of the application domains
  • Jan 22: Stefano: Presentation of the application domains
  • Jan 24: Dipankar Baisya: "DeepCpG accurate prediction of single-cell DNA methylation states using deep learning", Genome Biology, 2017.
  • Jan 26: Qihua Liang: "Using neural networks for reducing the dimensions of single-cell RNA-Seq data", NAR, 2017.
  • Jan 29: Isavannah Reyes: "Integrative Deep Models for Alternative Splicing", Bioinformatics, 2017.
  • Jan 31: Risul Islam: Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks, Sci Rep., 2016
  • Feb  2: Vishnu Priya: Gene expression inference with deep learning, Bioinformatics, 2016.
  • Feb  5: Lin Huang: "Maximum entropy methods for extracting the learned features of deep neural networks", PLOS Comp Bio, 2017.
  • Feb  7: Hao Chen: Removal of batch effects using distribution-matching residual networks, Bioinformatics, 2017
  • Feb  9: Gisel Bastidas Guacho: Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural Networks, Biorxiv, 2016
  • Feb 12: Shweti Mahajan, Deep learning with feature embedding for compound-protein interaction prediction, Biorxiv, 2016
  • Feb 14: Sanjana Sandeep, Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks, Bioinformatics, 2016
  • Feb 16: Cassio Elias, "Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters", JCB 2016
  • Feb 19: HOLIDAY
  • Feb 21: Chang Yuan, "DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins", Biorxiv
  • Feb 23: Dipan Shaw, "deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks", BCB 2016
  • Feb 26: Claudia Andrade, "Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Mode", PLoS Comp Bio, 2017
  • Feb 28: Uday Singh Saini: "Deep modeling of gene expression regulation in an Erythropoiesis model", ICML 2013
  • Mar  2: Al Amin Hossain: Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach", IEEE/ACM Trans Comput Biol Bioinform, 2015.
  • Mar  5: Lin Huang: DeepHINT: Understanding HIV-1 integration via deep learning with attention, Biorxiv, 2018
  • Mar  7: Isavannah Reyes: Predicting the impact of non-coding variants on DNA methylation, NAR, 2017
  • Mar  9: Dipankar Baisya: A deep learning framework for modeling structural features of RNA-binding protein targets, Nucleic Acids Research, 2016
  • Mar 12: Vishnu Priya: DeepChrome: deep-learning for predicting gene expression from histone modifications
  • Mar 14: Claudia Andrade: RaptorX-Property: a web server for protein structure property prediction, NAR 2016
  • Mar 16: Uday Singh Saini: CANCELLED