Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk - Liu, Fuxing; Peng, Lihong; Tian, Geng; Yang, Jialiang; Chen, Hui; Hu, Qi; Liu, Xiaojun; Zhou, Liqian (Frontiers in Bioengineering and Biotechnology, 2020)
Recently, many researches demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations, however, it is costly and time-consuming. Therefore, computational models are developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrated various biological information, credible negative sample selection, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and five-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained AUC value of 0.9829 with LOOCV and 0.9916 with five-fold cross validation on the SM2miR 1 dataset, and obtained AUC value of 0.8938 with LOOCV, and 0.9899 with five-fold cross validation on the SM2miR 2 dataset. More importantly, RWNS successfully captured 9, 17, 37 SMiR associations validated by experiments among the predicted top 10, 20 and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.