Information theoretic analysis of biological data

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.