Supervised posteriors for DNA-motif classification (original) (raw)

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 twod ifferent priors to facilitate parameter estimation using the maximum supervised posterior.Considering sevensets of eukaryotic transcription factor binding sites we find this approach to be superior employing area under the ROCcurveand false positive rate as performance criterion, and better in general using sensitivity.I naddition, we discuss potential reasons for the improvedperformance.