Inferring Transcription Factor interactions using a novel HV-SVM classifier
by Xiao-Li Li, Jun-Xiang Lee, Bharadwaj Veeravalli, See-Kiong Ng
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 1, No. 1, 2008

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.

Online publication date: Sat, 14-Jun-2008

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