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SUMMARY:Learning Probabilistic Sequence Models for Uncovering Gene Regulat
 ion - Mark Craven\, University of Wisconsin
DTSTART:20061018T151500Z
DTEND:20061018T161500Z
UID:TALK5596@talks.cam.ac.uk
CONTACT:Timothy G. Griffin
DESCRIPTION:A central challenge in computational biology is to uncover the
  mechanisms and cellular circuits that govern how the expression of variou
 s genes is controlled in response to a cell’s environment. In this talk\
 , I will discuss two aspects of my group’s work on learning probabilisti
 c grammars to identify gene-regulatory elements in genomic sequences. Firs
 t\, I will talk about a method we have developed for modeling and predicti
 ng arbitrarily overlapping elements in sequence data. We have applied this
  algorithm to the task of analyzing bacterial genomes\, in which it is com
 mon for functional elements in the genomes to overlap one another. Second\
 , I will talk about an approach we have developed for learning expressive 
 models of cis-regulatory elements (CRMs). A CRM is a configuration of sequ
 ence patterns that controls how a set of genes responds to specific condit
 ions in a cell. This talk will not assume any prior knowledge of molecular
  biology\, and for both algorithms\, I will discuss why they are of intere
 st for applications outside of biology.
LOCATION:Lecture Theatre 1\, Computer Laboratory
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