Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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Computational prediction of eukaryotic promoter is one of most elusive problems in DNA sequence analysis. Although considerable efforts have been devoted to this study and a number of algorithms have been developed in the last few years, their performances still need to further improve. In this work, we developed a new algorithm called PPFB for promoter prediction base on following hypothesis: promoter is determined by some motifs or word patterns and different promoters are determined by different motifs. We select most potential motifs (i.e. features) by divergence distance between two classes and constructed a classifier by feature boosting. Different from other classifier, we adopted a different training and classifying strategy. Computational results on large genomic sequences and comparisons with the several excellent algorithms showed that our method is efficient with better sensitivity and specificity.