Speaker: Alexis Battle, Stanford University
Title: Unraveling the genetics of disease using informed probabilistic models
Recent technological advances have allowed us to collect genomic data on an unprecedented scale, with the promise of revealing genetic variants, genes, and pathways disrupted in many diseases. However, identifying relevant genetic factors and ultimately unraveling the genetics of complex traits from such high dimensional data have presented significant statistical and computational challenges. In this domain, where spurious associations and lack of statistical power are major factors, I have developed machine learning methods based on robust probabilistic models that leverage biological structure, prior knowledge, and diverse sources of evidence. In particular, I have developed Bayesian methods that utilize structured information, including gene networks and cellular pathways, and transfer learning to propagate information across related genes and diseases. In this talk, I will discuss the application of such models to diverse traits including human disease and cellular traits derived from RNA-sequencing. Using this approach, I demonstrate an improvement in uncovering genetic variants affecting complex traits, along with the interactions and intermediate cellular mechanisms underlying genetic effects.
Alexis Battle is a PhD candidate in Computer Science at Stanford University. Her research in computational biology focuses on machine learning and probabilistic models for the genetics of complex traits. Alexis received her BS in Symbolic Systems from Stanford, and spent four years as a member of the technical staff at Google. She is the recipient of an NSF Graduate Research Fellowship and an NIH NIGMS training award.
This is a joint talk with the Donnelly Centre for Cellular and BioMolecular Research and the Department of Comptuer Science
For additional information contact: Fritz Roth