Seminars

Understanding protein function and modeling protein-protein interactions in biological networks is a key challenge in modern systems biology.  Recent developments in biotechnology have enabled  high-throughput measurement of several cellular phenomena including  gene expression, protein-protein interactions, protein localization,  and sequence.  The wealth of data generated by such technology  promises to support computational prediction of network models, but so far, successful approaches that translate these data into accurate, experimentally testable hypotheses have been limited.
I will discuss key insights into why we face this imbalance  between genomic data and established knowledge and present  computational approaches for addressing these challenges.   Specifically, I will focus on methods for measuring genomic dataset  reliability and illustrate how reliability often varies across  different biological contexts.  We have developed a Bayesian  framework for leveraging this variation to improve network prediction  accuracy and implemented this approach in a public, web-based system  for user-driven search and visualization of genomic data.  I will  describe the supporting machine learning methods as well as important  data visualization features, which play a critical role in making the  system practical.  To illustrate the power of our approach, I will  demonstrate how we have used it to correctly predict function for  several previously uncharacterized genes in yeast and to elucidate  the behavior of Hsp90, a target of recent cancer drugs.  I will close  with a brief overview of plans for future research motivated by this  work.

Bio: Chad Myers grew up in North Dakota and graduated from Beulah HS in  1998.  He completed his undergrad education at Southern Methodist U.  in Dallas where he studied Computer Engineering, Physics, and Math.   He is currently a Ph.D. candidate in Computer Science and the Lewis- Sigler Institute for Integrative Genomics at Princeton U.  His  research interests are computational biology, machine learning, and  signal processing.   http:// www.cs.princeton.edu/~clmyers/