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Supervised Posteriors for DNA-motif Classification
2007
German Conference on Bioinformatics
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
dblp:conf/gcb/GrauKKGP07
fatcat:6vsev4bkarg5zcfstqcbxe5nu4