RWRMDA: predicting novel human microRNA–disease associations

Abstract

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.