View More
View Less
System Message
An unknown error has occurred and your request could not be completed. Please contact support.
Wait Listed
Personal Calendar
Conference Event
Schedule TBD
Conflict Found
This session is already scheduled at another time. Would you like to...
Please enter a maximum of {0} characters.
Please enter a maximum of {0} words.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Replies ()
New Post
Microblog Thread
Post Reply
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
2017 GTC San Jose

S7633 - Interpretable, Integrative Deep Learning for Decoding the Human Genome

Session Speakers
Session Description

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 units of DNA sequence that encode for proteins. But genes 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 variations 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 encoded in millions of hierarchically organized units of regulatory elements. These functional words and grammars are sparsely distributed across the genome 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 variety of blood cell types. Our models also allow us to predict the effects of natural and disease-associated genetic variation, that is, how changes in the DNA sequence are likely to affect molecular mechanisms associated with various complex diseases such as coronary heart disease.

Additional Session Information
AI in Healthcare Summit Computational Biology Deep Learning and AI Healthcare and Life Sciences
Healthcare & Life Sciences
25 minutes
Session Schedule