Predicting microRNA-disease associations by walking on four biological networks

Abstract

MicroRNA(miRNA) plays an important role in regulating the expression of target mRNAs. The deregulation of microRNAs appears to associate with various diseases. Recently, researchers focus on making use of various biological properties to identify the associations between microRNAs and diseases so as to provide helpful information for disease therapies. Accumulate evidences have shown that the inter- and intra-relationships of microRNAs, diseases, environment factors and genes contribute to correctly detect candidate microRNA-disease associations. However, there lack of methods that can comprehensively make use of the advantage of these relationships. In this work, we construct four separate biological networks, that are microRNA functional similarity network(MFN), disease semantic similarity network(DSN), environmental factor chemical structure similarity network(ESN) and gene-gene functional similarity network( GSN). After that, an unbalanced four random walking method, namely FourRW is implemented on the four networks, which not only can flexibly infer information from different levels of neighbors in the four networks, but also realizes the information transfer between different networks. The results of experiment show that our method achieves better prediction performance than the other state-of-the-art methods.