Deep Learning in Genomics and Biomedicine

Deep Learning in Genomics and Biomedicine

Course Overview

Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results.  In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will work in groups on a final class project using real world datasets.

Prerequisites

College calculus, linear algebra, basic probability and statistics such as CS109, and basic machine learning such as CS229. No prior knowledge of genomics is necessary.

Lecture Venue and Times

09/26/2016 - 12/09/2016 Mon, Wed 3:00 PM - 4:20 PM at Hewlett Teaching Center 201

Recitation. Fridays 10:30am - 11:20am at Hewlett 102

Instructors

Anshul Kundaje, Assistant Professor (akundaje@stanford.edu)

James Zou, Assistant Professor ()

 

Office hours

James Zou: Wednesdays 5-7pm (Packard 253).

Anna Shcherbina and Nadine Hussami: Mondays 5-7pm (Lane L339)

Jayanth and Alon: Thursdays 10:30am-12:30pm (Huang Basement)

Assignments

Course project (50%): the students will form teams of 4-6 and choose from one of the suggested projects or select their own project. Teams will be given Microsoft Azure credits to implement algorithms and perform analysis. Teams are expected to work on the research project throughout the second half of the quarter and produce conference-style papers. Each team will present the paper to the entire class at the end of the semester.

A significant portion of the class will be based on reading and discussing the latest literature. Every student should read the assigned papers before class and participate in discussions.

Paper presentation (20%): each team selects one of the suggested papers to present in detail to the class.

Paper review (20%): each team selects 2 other papers to review. The review concisely summarize the key findings of the paper, highlight interesting ideas, weaknesses and potential extensions.

Class participation and quizzes (10%): every student should actively engage in paper discussions in class and in the online forum. We will also have a few in class quizzes.

Tentative outline

[10 weeks of instruction; 20 classes]

Module 1: introduction to deep learning and demos (7 classes).

Module 2: Applications of deep learning to regulatory genomics, variant scoring and population genetics (4 classes)

Module 3: Applications of deep learning to predicting protein structure and pharmacogenomics (3 classes)

Module 4: Applications of deep learning to electronic health records and medical imaging data (4 classes)

Project presentations (Exam period)

Date Topic Primary instructor papers paper URLs Other relevant links
9/26 Intro to neural networks, backprop  James Stegle Review http://msb.embopress.org/content/12/7/878 , http://neuralnetworksanddeeplearning.com/
9/28 Convolutional neural network + intro to func genomics Anshul DeepBind

http://www.nature.com/nbt/journal/v33/n8/full/nbt.3300.html,

10/3 Conv nets for genomics and imaging (contd) Anshul, Serafim DeepCpG http://biorxiv.org/content/early/2016/05/27/055715
10/5 Interpretation of deep learning models Avanti See Files Section
10/10 Recurrent neural network + autoencoders + EHR data James DeepNano: Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads http://arxiv.org/abs/1603.09195
10/12 Training deep neural networks + protein structures James
10/17 Azure, Tensorflow and Keras demo James
10/19 Func Genomics/Variant/PopGen James

DeepSEA

Basset

http://www.nature.com/nmeth/journal/v12/n10/full/nmeth.3547.html


http://genome.cshlp.org/content/26/7/990

10/24 Func Genomics/Variant/PopGen James

DanQ

The human splicing code reveals new insights into the genetic determinants of disease

http://nar.oxfordjournals.org/content/44/11/e107

http://sites.utoronto.ca/intron/xiong2015.pdf

10/26 Func Genomics/Variant/PopGen Anshul

DeepGDashboard

Learning structure in gene expression data using deep
architectures, with an application to gene clustering 

http://arxiv.org/abs/1608.03644
http://biorxiv.org/content/biorxiv/early/2015/11/16/031906.full.pdf
10/31 PopGen, Small molecules James, Serafim

Deep Learning for Pop Gen Inference 

Automatic chemical design using a data-driven continuous representation of molecules

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004845

https://arxiv.org/pdf/1610.02415v1.pdf

11/2 Protein Structure James

Protein secondary structure prediction using deep convolutional neural fields

 

https://arxiv.org/abs/1512.00843

 

11/7 Project proposal James, Serafim project proposal lightning talks https://github.com/greenelab/deep-review/issues/45
11/9 Pharmacogenomics James

Convolutional LSTM Networks for Subcellular Localization of Proteins

Protein contact map prediction using ultra deep residual nets

 

https://arxiv.org/pdf/1503.01919.pdf

http://biorxiv.org/content/early/2016/09/06/073239

DeepTox: Toxicity Prediction using Deep Learning: http://journal.frontiersin.org/article/10.3389/fenvs.2015.00080/full
11/14 Pharmacogenomics Anshul

Molecular Graph Convolutions: Moving Beyond Fingerprints

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

Written presentation only:
Massively Multitask Networks for Drug Discovery

https://arxiv.org/abs/1603.00856

 

https://arxiv.org/abs/1510.02855


https://arxiv.org/pdf/1502.02072v1.pdf

11/16 Med Records/Clinical data Anshul, Serafim

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records; 

Deep Kalman Filters

http://www.nature.com/articles/srep26094

 

https://arxiv.org/abs/1511.05121

11/28 Med Records/Clinical data Serafim, James

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Deep Survival analysis

http://link.springer.com/chapter/10.1007%2F978-3-319-31750-2_3

http://arxiv.org/pdf/1608.02158v1.pdf

11/30 Medical Imaging Anshul, James

DeepCyTOF: Automated Cell Classification of Mass Cytometry Data by Deep Learning and Domain Adaptation


Microscopy cell counting and detection with fully convolutional regression networks

http://biorxiv.org/content/early/2016/06/14/054411,


http://www.tandfonline.com/doi/full/10.1080/21681163.2016.1149104

12/5 Medical Imaging Anshul

Deep Learning for Identifying Metastatic Breast Cancer

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

https://arxiv.org/abs/1606.05718

http://arxiv.org/abs/1603.05959

12/7 Wrap up TAs
Exam period Project presentations Anshul, James

 

Course Summary:

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