Invited Talks

LEARNING PREDICTIVE MODELS OF GENE REGULATION

Abstract
Studying the behavior of gene regulatory networks by learning from high-throughput genomic data has become one of the central problems in computational systems biology. Most work in this area focuses on learning structure from data -- e.g. finding clusters or modules of potentially co-regulated genes, or building a graph of putative regulatory "edges" between genes -- and generating qualitative hypotheses about regulatory networks.

Instead of adopting the structure learning viewpoint, our focus is to build predictive models of gene regulation that allow us both to make accurate quantitative predictions on new or held-out experiments (test data) and to capture mechanistic information about transcriptional regulation. Our algorithm, called MEDUSA, integrates promoter sequence, mRNA expression, and transcription factor occupancy data to learn gene regulatory programs that predict the differential expression of target genes. MEDUSA does not rely on clustering or correlation of expression profiles to infer regulatory relationships. Instead, the algorithm learns to predict up/down expression of target genes by identifying condition-specific regulators and discovering regulatory motifs that may mediate their regulation of targets. We use boosting, a technique from machine learning, to help avoid overfitting as the algorithm searches through the high dimensional space of potential regulators and sequence motifs. We will describe results of a recent gene expression study of hypoxia in yeast, in collaboration with the lab of Li Zhang. We used MEDUSA to propose the first global model of the oxygen and heme regulatory network, including new putative context-specific regulators. We then performed biochemical experiments to confirm that regulators identified by MEDUSA indeed play a causal role in oxygen regulation.

Research Interests
Computational Biology and Machine Learning. My research involves applying machine learning and data mining techniques -- like support vector machines, kernel methods, and probabilistic modeling -- to analyze biological data, including protein and DNA sequences and gene expression data from microarray experiments.

Professional Experience and Education
2004-present Research Scientist, Center for Computational Learning Systems, Columbia University

2002-present Faculty, Center for Computational Biology and Bioinformatics, Columbia University

2000-2003 Assistant Professor, Department of Computer Science, Columbia University

1999-2000 Postdoctoral Research Fellow, Department of Mathematics, Columbia University

Education
1998 University of California, Berkeley, Ph.D. (Mathematics)
1992 University of Waterloo (Canada), B.Math.