March 5, 2007 - Alan Qi

Title
Bayesian Learning for Deciphering Gene Regulation

Abstract
Gene regulation plays a fundamental role in biological systems. As more high-throughput biological data becomes available it is possible to quantitatively study gene regulation in a systematic way. In this talk I will present my work on three problems related to gene regulation: (1) identifying genes that affect organism development; (2) detecting protein-DNA binding events and cis-regulatory elements; (3) and deciphering regulatory cascades at the transcriptional level for stem cell development. To address these problems, I developed novel nonparametric Bayesian models, Bayesian semi-supervised learning methods, and approximate inference methods for loopy graphs. These methods capture key aspects of biological processes and make functional predictions, some of which were confirmed by biological experiments. I will conclude with a brief description of my plan for future research in computational biology and Bayesian learning.