Title: "Bayesian Machine Learning for Deciphering Gene Regulation"
Speaker: Alan Qi, Department of Computer Science, Department of Statistics, Department of Biology (courtesy), Purdue University

Place: Mechanical Engineering (ME) 161; September 11, 2007, Tuesday, 4:30pm

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 we present our work on two related problems on gene regulation including: (1) identifying genes that affect organism development, and (2) detecting protein-DNA binding events and /cis/-regulatory elements. To address these problems, we must overcome many computational challenges, including learning with little prior biological knowledge and inference with joint effect of many biological variables.

Facing these computational challenges, we first devised a novel Bayesian semi-supervised classification method to identify candidate genes specific to certain lineages and cell-types of /C. elegans/ embryos. Our computational predictions about some previously uncharacterized genes were experimentally confirmed by my biologist collaborators. Second, we built a new Bayesian graphical model of protein-DNA binding and developed an approximate inference algorithm to efficiently estimate binding events in high spatial-resolution and guide motif discovery. The software implementation of this algorithm is being used by research groups worldwide.



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