SeHNE: Semi-supervised Heterogeneous Network Embedding for Drug Combination

Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in therapy of many complex diseases. Although great efforts have been devoted to the prediction of single drugs, the identification of drug combination is really limited. The current algorithms assume the independence of features and prediction, resulting in an undesirable performance. To address this issue, we develop a novel semi-supervised heterogeneous network embedding algorithm (called SeHNE) to predict drug combinations, where ATC similarity of drugs, drug-target and protein-protein interaction (PPI) networks are integrated to construct heterogeneous network. SeHNE jointly learns features of drugs by exploiting the topological structure of heterogeneous networks, and prediction of drug combination. One typical advantage of SeHNE is that features are extracted under the guidance of classification, thereby improving the accuracy of algorithms. Experimental results demonstrate that proposed algorithm is more accurate than state-of-the-art methods on the dataset we collected, and the re-training process could improve the accuracy of classifier.