Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.

Recently, more and more research has shown that microRNAs (miRNAs) play critical roles in the development and progression of various diseases, but it is not easy to predict potential human miRNA-disease associations from the vast amount of biological data. Computational methods for predicting potential disease-miRNA associations have gained a lot of attention based on their feasibility, guidance and effectiveness. Differing from traditional local network similarity measures, we adopted global network similarity measures and developed Random Walk with Restart for MiRNA-Disease Association (RWRMDA) to infer potential miRNA-disease interactions by implementing random walk on the miRNA-miRNA functional similarity network. We tested RWRMDA on 1616 known miRNA-disease associations based on leave-one-out cross-validation, and achieved an area under the ROC curve of 86.17%, which significantly improves previous methods. The method was also applied to three cancers for accuracy evaluation. As a result, 98% (Breast cancer), 74% (Colon cancer), and 88% (Lung cancer) of top 50 predicted miRNAs are confirmed by published experiments. These results suggest that RWRMDA will represent an important bioinformatics resource in biomedical research of both miRNAs and diseases.