Chris Park will present his preFPO on Tuesday February 26 at 1:30PM in Room 402. The members of his committee are: Olga Troyanskaya, advisor; Mona Singh and Kai Li, readers; Rob Schapire and Moses Charikar, nonreaders. Everyone is invited to attend his talk. His abstract follows below. ---------------------------------------------------------------- Title: Inferring gene function and heterogeneous gene networks from genomic data Abstract With the advancement of quantitative biology, there has been a dramatic increase in functional genomic data. Using these genomic datasets which includes protein-protein interaction, gene expression, localization, and gene knockout studies has enabled researchers to construct functional gene networks. In functional gene networks, each edge represents the probabilistic support for a functional relationship between two genes. However in practice, the goal of biology is to understand further depth of the function and relationship between genes, beyond the abstract notion of a functional relation. In this work, we expand the depth and breadth of functional gene networks. First, we demonstrate that functional gene networks can improve the transfer of gene annotations between organisms to allow prediction algorithms to identify additional genes participating in biological processes that are not already well studied. We apply our novel functional knowledge transfer method to accurately predict novel gene functions across six diverse organisms. Second, biomolecular pathways are built from diverse types of pairwise interactions, ranging from physical protein-protein interactions and modifications to indirect regulatory relationships. One goal of systems biology is to bridge three aspects of this complexity: the growing body of high-throughput data assaying these interactions; the specific interactions in which individual genes participate; and the genome-wide patterns of interactions in a system of interest. Here, we describe methodology for predicting specific types of biomolecular interactions, beyond functional relationship, using high-throughput genomic data in both yeast and human. Ultimately, our work provides further insight into the genetic wiring of the cell enabled by large-scale machine learning with genome-wide biological data.