Supervised posteriors for DNA-motif classification

Abstract

Markov models have been proposed for the classification of DNA-motifs using generative approaches for parameter learning. Here, we propose to apply the discriminative paradigm for this problem and study two different priors to facilitate parameter estimation using the maximum supervised posterior. Considering seven sets of eukaryotic transcription factor binding sites we find this approach to be superior employing area under the ROC curve and false positive rate as performance criterion, and better in general using sensitivity. In addition, we discuss potential reasons for the improved performance.