Statistical Optimality in Bioinformatics: Theory vs Practice Organizer: Rudy Guerra, Rice University Chair: Rudy Guerra, Rice University Session summary Bioinformatics is in
large part defined by the collection, storage and analysis of large
biological data sets, which in most cases correspond to some aspect of
DNA sequences. The substantive biological or biomedical problems
underlying the data are often complex and not amenable to standard
statistical models and analysis. As such, computational and statistical
algorithms have found much application in bioinformatics. Such
algorithms may be founded on basic biological principles, but their
statistical behavior is usually limited to knowledge obtained from
simulation. This session addresses the role of statistical optimality
in bioinformatic problems. More generally, in the face of a complex
scientific question, how do statisticians proceed to provide the "best"
analysis possible when it may difficult to even cast the problem under
a formal statistical framework leading to "optimal" results.
