GA_SVM: A Genetic Algorithm for Improving Gene Regulatory Activity Prediction

Abstract

Gene regulatory activity prediction problem is one of the important steps to understand the significant factors for gene regulation in biology. The advents of recent sequencing technologies allow us to deal with this task efficiently. Amongst these, Support Vector Machine (SVM) has been applied successfully up to more than 80% accuracy in the case of predicting gene regulatory activity in Drosophila embryonic development. In this paper, we introduce a metaheuristic based on genetic algorithm (GA) to select the best parameters for regulatory prediction from transcriptional factor binding profiles. Our approach helps to improve more than 10% accuracy compared to the traditional grid search. The improvements are also significantly supported by biological experimental data. Thus, the proposed method helps boosting not only the prediction performance but also the potentially biological insights.