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SUMMARY:Recognition of transcription factor binding sites with variable or
 der Bayesian networks - Ivo Grosse\, Martin Luther University\, Halle
DTSTART:20071010T100000Z
DTEND:20071010T110000Z
UID:TALK8632@talks.cam.ac.uk
CONTACT:Michael Hoffman
DESCRIPTION:One of the problems in DNA sequence analysis is the recognitio
 n of _cis_-regulatory modules and the reconstruction of their evolution.  
 Many of the existing algorithms\, including those for phylogenetic footpri
 nting\, use statistical models\, such as Markov models and Bayesian networ
 ks\, for the recognition of transcription factor binding sites.  One disad
 vantage of these models is the exponential growth of the number of model p
 arameters with the context length and the resulting danger of over-fitting
 .  In an attempt to circumvent this problem\, we propose variable order Ba
 yesian networks\, and we find that they can improve the recognition of bin
 ding sites of several transcription factors.
LOCATION:M203\, Cairns Pavillion\, Wellcome Trust Genome Campus\, Hinxton
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