Title: Inferring Transcription Factor interactions using a novel HV-SVM classifier

Authors: Xiao-Li Li, Jun-Xiang Lee, Bharadwaj Veeravalli, See-Kiong Ng

Addresses: Data Mining Department, Institute for Infocomm Research, 119613, Singapore. ' Department of Electrical and Computer Engineering, National University of Singapore, 117576, Singapore. ' Department of Electrical and Computer Engineering, National University of Singapore, 117576, Singapore. ' Data Mining Department, Institute for Infocomm Research, 119613, Singapore

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.

Keywords: transcription factor; TF-TF interactions; support vector machines; SVM classifier; protein domains; GO annotations; transcription regulation; eukaryotes; genetic algorithms; GAs.

DOI: 10.1504/IJCBDD.2008.018710

International Journal of Computational Biology and Drug Design, 2008 Vol.1 No.1, pp.59 - 73

Available online: 14 Jun 2008 *

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