Inferring biological networks from diverse genomic data
Computational Systems Biology Cluster Hire Candidate
Monday, March 5, 2007
10:30 a.m. - 1111 Biotechnology Center Auditorium
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/
Return to seminar list