[talks] C Park preFPO

Melissa M. Lawson mml at CS.Princeton.EDU
Thu Feb 21 15:24:38 EST 2013

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

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.

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