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An HV-SVM Classifier to Infer TF-TF Interactions Using Protein Domains and GO Annotations

Interactions between transcription factors (TFs) are necessary for deciphering the complex mechanisms of transcription regulation in eukaryotes. In this paper, we proposed a novel HV-kernel based Support Vector Machine classifier (HV-SVM) to predict TF-TF interactions based on their protein domain information and GO annotations. Specifically, 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 optimize the classifier's performance. We applied our proposed HV-SVM method to predict TF interactions for Homo sapiens and Mus muculus. We obtained accuracy and F-measures of over 85% and an AUC of almost 93%, demonstrating that HV-SVM can accurately predict TF-TF interactions even in the higher and more complex eukaryotes.