title: BoCaTFBS: a boosted cascade learner to refine the binding sites suggested by ChIP-chip experiments.
abstract: Comprehensive mapping of transcription factor binding sites is essential in postgenomic biology. For this, we propose a mining approach combining noisy data from ChIP (chromatin immunoprecipitation)-chip experiments with known binding site patterns. Our method (BoCaTFBS) uses boosted cascades of classifiers for optimum efficiency, in which components are alternating decision trees; it exploits interpositional correlations; and it explicitly integrates massive negative information from ChIP-chip experiments. We applied BoCaTFBS within the ENCODE project and showed that it outperforms many traditional binding site identification methods (for instance, profiles).
authors.affiliation: Integrated Data Systems Department, Siemens Corporate Research, 755 College Road East, Princeton, New Jersey 08540, USA.
authors.forename: Lu-yong
authors.initials: LY
authors.lastname: Wang
authors.forename: Michael
authors.initials: M
authors.lastname: Snyder
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics / Q000378:metabolism; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D008969:Molecular Sequence Data; D012372:ROC Curve; D015203:Reproducibility of Results; D020218:Response Elements / Q000235:genetics*; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors; D004247:DNA