title: Inferring transcription factor interactions using a novel HV-SVM classifier.
abstract: Interactions between Transcription Factors (TFs) are necessary for deciphering the complex mechanisms of transcription regulation in eukaryotes. We proposed a novel HV-kernel based SVM classifier to classify TF-TF pairs based on their protein domains and GO annotations. Two types of pairwise kernels, namely, a horizontal kernel and a vertical kernel, were combined to evaluate the similarity between a pair of TFs, and a Genetic Algorithm was used to obtain kernel and feature weights to optimise the classifier's performance. We showed that our proposed HV-SVM method can make accurate predictions of TF-TF interactions even in the higher and more complex eukaryotes.
authors.affiliation: Data Mining Department, Institute for Infocomm Research, 119613, Singapore. xlli@i2r.a-star.edu.sg
authors.forename: Xiao-Li
authors.initials: XL
authors.lastname: Li
authors.forename: Jun-Xiang
authors.initials: JX
authors.lastname: Lee
authors.forename: Bharadwaj
authors.initials: B
authors.lastname: Veeravalli
authors.forename: See-Kiong
authors.initials: SK
authors.lastname: Ng
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D051379:Mice; D008954:Models, Biological; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D014157:Transcription Factors / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D014157:Transcription Factors