title: Probabilistic models for semisupervised discriminative motif discovery in DNA sequences.
abstract: Methods for discriminative motif discovery in DNA sequences identify transcription factor binding sites (TFBSs), searching only for patterns that differentiate two sets (positive and negative sets) of sequences. On one hand, discriminative methods increase the sensitivity and specificity of motif discovery, compared to generative models. On the other hand, generative models can easily exploit unlabeled sequences to better detect functional motifs when labeled training samples are limited. In this paper, we develop a hybrid generative/discriminative model which enables us to make use of unlabeled sequences in the framework of discriminative motif discovery, leading to semisupervised discriminative motif discovery. Numerical experiments on yeast ChIP-chip data for discovering DNA motifs demonstrate that the best performance is obtained between the purely-generative and the purely-discriminative and the semisupervised learning improves the performance when labeled sequences are limited.
authors.affiliation: Department of Computer Science, Pohang University of Science and Technology, San 31, Hyoja-dong, Nam-gu, Pohang 790-784, Korea. blkimjk@postech.ac.kr
authors.forename: Jong Kyoung
authors.initials: JK
authors.lastname: Kim
authors.forename: Seungjin
authors.initials: S
authors.lastname: Choi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry*; D004271:DNA, Fungal / Q000235:genetics; D030541:Databases, Genetic; D008957:Models, Genetic; D015233:Models, Statistical; D059372:Nucleotide Motifs; D020411:Oligonucleotide Array Sequence Analysis; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors
chemical_list: D004271:DNA, Fungal; D014157:Transcription Factors; D004247:DNA