Predicting Therapeutic Targets with Integration of Heterogeneous Data Sources

Drug target is of great importance for designing new drugs and understanding the molecular mechanism of drug actions. In general, a drug may bind to multiple proteins, some of which are not related to disease-treatment or even lead to side effects. Therefore, it is necessary to discriminate the effect-mediating drug targets, i.e. therapeutic targets, from other proteins. Although a lot of computational approaches have been developed to predict drug targets and achieve partial success, few attention has been paid to predict therapeutic targets. In this work, we present a new framework to predict drug therapeutic targets based on the integration of heterogeneous data sources. In particular, we develop an ensemble classifier, PTEC (Predicting Therapeutic targets with Ensemble Classifier), that can effeciently integrate both drug and protein properties described from distinct perspectives, thereby improving prediction accuracy. The results on benchmark datasets demonstrate that our approach outperforms other popular approaches significantly, implying the effectiveness of our proposed approach. Furthermore, the results indicate that the integration of different data sources can not only improve the coverage of predicted targets but also the prediction precision. In other words, distinct data sources indeed complement with each other, and the integration of these heterogeneous data sources can improve the prediction accuracy.