APPLIED MATH SEMINAR

Speaker: Noam Slonim
         Princeton University

Title:  Information theoretic analysis of biological data

When/where: Wed, Nov. 30th, 4:15pm, AKW 500

Abstract:

In recent years, researchers have been facing a rapid increase in the
available biological data.
These data come in a variety of forms - complete genome sequences, mRNA
transcriptional profiles,
protein-protein interactions, and so forth. Automatic data analysis
methods are often the only route for
extracting meaningful insights out of these data. Existing techniques,
however, typically employ nontrivial assumptions.
These assumptions might be explicit, as in assuming a specific model
which reflects one's prior beliefs about the data;
or implicit, as in arbitrarily specifying a correlation or a
``similarity'' measure which lies at the core of any further analysis.
While it is clear that such assumptions should be avoided, the
conventional wisdom
is that in practice they are actually unavoidable. In this talk I will
describe an
information theoretic framework that allows to extract biologically
important insights without any
prior assumptions about the nature of the data for a wide variety of
problems.
I will briefly discuss several recent applications of this approach, and
will present in more detail results
for systematic genotype-phenotype association in bacteria and archaea.