Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences
Abstract
Position weight matrices (PWMs) are the standard model for DNA and RNA regulatory motifs. In PWMs nucleotide probabilities are independent of nucleotides at other positions. Models that account for dependencies need many parameters and are prone to overfitting. We have developed a Bayesian approach for motif discovery using Markov models in which conditional probabilities of order k - 1 act as priors for those of order k This Bayesian Markov...
Paper Details
Title
Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences
Published Date
Jun 9, 2016
Journal
Volume
44
Issue
13
Pages
6055 - 6069
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