Interpretable, integrative deep learning for decoding the human genome

Anshul Kundaje, Stanford University

Date & Time: Wednesday, August 1, 2018, 6:00 PM – 8:00 PM PDT

Location: Intel SC9 – Auditorium(Santa Clara 9), 2250 Mission College Blvd, Santa Clara, CA 95054

Registration Fee: IEEE CIS members: free 
         Students – free 
         IEEE (non-CIS) members – free 
         Non-members – free

Event Co-sponsor:

• IEEE SCV Engineering in Medicine and Biology Society (EMBS)

Abstract: The human genome contains the fundamental code that defines the identity and function of all the cell types and tissues in the human body. Genes are functional sequence units that encode for proteins. But they account for just about 2% of the 3 billion long human genome sequence. What does the rest of the genome encode? How is gene activity controlled in each cell type? Where do the regulatory control elements lie and what is their sequence composition? How do variants and mutations in the genome sequence affect cellular function and disease? These are fundamental questions that remain largely unanswered. The regulatory code that controls gene activity is made up complex genome sequence grammars representing hierarchically organized units of regulatory elements. These functional words and grammars are sparsely distributed across billions of nucleotides of genomic sequence and remain largely elusive. Deep learning has revolutionized our understanding of natural language, speech and vision. We strongly believe it has the potential to revolutionize our understanding of the regulatory language of the genome. We have developed integrative supervised deep learning frameworks to learn how genomic sequence encodes millions of experimentally measured regulatory genomic events across 100s of cell types and tissues. We have developed novel methods to interpret our models and extract local and global predictive patterns revealing many insights into the regulatory code. We demonstrate how our deep learning models can reveal the regulatory code that controls differentiation and identity of diverse blood cell types. Our models also allow us to predict the effects of natural and disease-associated genetic variation i.e. how differences in DNA sequence across healthy and diseased individuals are likely to affect molecular mechanisms associated with complex traits and diseases.

Biography: Anshul Kundaje is an Assistant Professor of Genetics and Computer Science at Stanford University. The Kundaje lab develops statistical and machine learning methods for large-scale integrative analysis of functional genomic data to decode regulatory elements and pathways across diverse cell types and tissues and understand their role in cellular function and disease. Anshul completed his Ph.D. in Computer Science in 2008 from Columbia University. As a postdoc at Stanford University from 2008-2012 and a research scientist at MIT and the Broad Institute from 2012-2014, he led the integrative analysis efforts for two of the largest functional genomics consortia – The Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Project. Dr. Kundaje is a recipient of the 2016 NIH Director’s New Innovator Award and The 2014 Alfred Sloan Foundation Fellowship.

Speaker(s): Anshul Kundaje,

Location:
Intel SC-9 Auditorium 2250 Mission College Blvd
Santa Clara, California
95054