A Hybrid Markov Random Field Model for Drug Interaction Prediction

Drug interactions represent adverse effects when employing two or multiple drugs together in treatments. Adverse effects are critical and may be deadly in medical practice. However, our understanding of drug interactions is far from complete. In the medical study on drug interaction, the prediction of potential drug interactions will help reducing the experimental efforts. In this paper, we extend a hinge-loss Markov Random Field Model and propose hybrid model of it and logistic regression. In the model, we combine multiple types of chemical and biological evidence to infer the interactions between drugs. Logistic regression is used to learn weights of those evidences. Experiments shows that our approach achieves better performance than the state-of-the-art approaches on both prediction accuracy and time efficiency.