BACK TO INDEX
Publications of Eduardo D. Sontag jointly with X. Yu
Articles in journal or book chapters
and A. Levine.
The P53HMM algorithm: using novel profile Hidden Markov Models to detect p53-responsive genes.
Keyword(s): Hidden Markov Models,
A novel computational method (called p53HMM) is presented that utilizes Profile Hidden Markov Models (PHMM's) to estimate the relative binding affinities of putative p53 response elements (RE's), both p53 single-sites and cluster-sites. These models incorporate a novel ``Correlated Baum Welch'' training algorithm that provides increased predictive power by exploiting the redundancy of information found in the repeated, palindromic p53-binding motif. The predictive accuracy of these new models are compared against other predictive models, including position specic score matrices (PSSM's, or weight matrices). Finally, we provide experimental evidence that verifies a predicted p53-target site that regu- lates the CHMP4C gene. The P53HMM algorithm is available on-line from http://tools.csb.ias.edu.
BACK TO INDEX
This material is presented to ensure timely dissemination of
scholarly and technical work. Copyright and all rights therein
are retained by authors or by other copyright holders.
Last modified: Mon Dec 30 13:54:44 2019
This document was translated from BibTEX by