Title: A study of statistical methods for function  prediction of protein motifs

Speaker: Tao Tao, Department of Computer Science, UIUC Place: Siebel Center 3405 Time: 11am-12noon

Abstract:

Automatic discovery of new protein motifs (i.e.,  amino acid patterns) is one of the major challenges in  bioinformatics. Several algorithms have been proposed that  can extract statistically significant motif patterns from  any set of protein sequences. With these methods, one can  generate a large set of candidate motifs that may be  biologically meaningful. In this paper, we study several  statistical methods to automatically predict the functions  of these candidate motifs, including a popularity method, a  mutual information method, and statistical translation  models. These methods capture, from different perspectives,  the correlations between the matched motifs of a protein and  its assigned Gene Ontology(GO) terms, which characterize the  function of the protein. We evaluate these different methods  using the known motifs in the Interpro database. Each method  is used to rank candidate terms for each motif. We, then,  use mean reciprocal rank(MRR) to evaluate the performance.  The results show that in general, all these methods perform  well, suggesting that they can all be useful for predicting  an unknown motif s function. Among all the methods tested, a  statistical translation model with popularity prior performs  the best.

Bio:

Tao Tao is a Ph.D. candidate in the Computer Science  Department at University of Illinois at Urbana-Champaign.  He has been working on information retrieval models,  text/data mining with applications to bioinformatics.