Chad Myers will present his preFPO on Friday February 9 at 3:30 PM in
Carl Icahn Lab Room 200. The members of his committee are: Olga
Troyanskaya, advisor; Kai Li and David Botstein (MolBio, Genomics),
readers; SY Kung (ELE) and Leonid Kruglyak (EEB, Genomics),
non-readers. Everyone is invited to attend his talk. His abstract follows
below.
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Title: Context-sensitive methods for learning from genomic data
Abstract:
Recent developments in biotechnology have enabled high-throughput measurement of several
complementary cellular phenomena. The wealth of data generated by such technology promises
to support computational prediction of network models, but so far, successful approaches
that translate these data into accurate, experimentally testable hypotheses have been
limited. My thesis focuses on machine learning and signal processing approaches that
utilize contextual clues that often accompany biological data to extract useful
information and make precise predictions.
First, my thesis describes methods for using microarray technology to detect chromosomal
aberrations. Amplification and deletion of portions of chromosomes often serves as a
mechanism of rapid adaptation and have been associated with numerous cancers. Accurate
and precise identification of when and where these changes occur will help us understand
this important adaptive mechanism and enable steps towards effective cancer treatment. I
discuss my solution to this problem, ChARM (Chromosomal Aberration Region Miner), a
statistical signal processing approach based on expectation-maximization that uses
chromosome context information to accurately identify even subtle chromosomal changes from
either gene expression or CGH microarray data.
Second, I have addressed the more general problem of integrating diverse types of
functional genomic data (e.g. gene expression, protein-protein interactions, genetic
interactions, sequence, and protein localization data) to understand gene function and
predict biological networks. I discuss a system we have developed for integration of
these diverse data and user-driven network inference. My key contribution in this area is
the notion of query context-sensitive prediction. This idea is based on the observation
that most experimental technologies capture different biological processes with varying
degrees of success, and thus, each source of genomic data will vary in relevance depending
on the biological process one is interested in predicting. Other key contributions of
this work are the data visualization approaches that support intelligent, expert browsing
of genomic data, which is a largely unexplored, but powerful paradigm in bioinformatics
applications. I discuss evaluation of these methods and examples of biological
validation, where we have used our system to characterize several new genes.
Finally, my thesis addresses the question of how to use machine learning and other
bioinformatics methods to direct large-scale genomic experiments. Until now, most
bioinformatics methods have been applied downstream of data-generating experiments,
serving mainly as tools for analysis. I discuss methods for directing large-scale
experiments in the context of whole-genome genetic interaction screens. We have applied
these methods in collaboration with experimental labs, and we demonstrate that such
approaches enable more efficient use of high-throughput technology and, ultimately, help
us to learn more novel biology.
