论文题目：RWRMDA: predicting novel human microRNA-disease associations

论文作者：Xing Chen, Ming-Xi Liu, and Gui-Ying Yan(闫桂英)

论文摘要: Recently, more and more evidences have 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 vast amount of biological data. Computational methods for predicting potential disease-miRNA associations have been paid greater attention based on their feasibility, guidance and effectiveness. Different 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.