Identification of Small Molecule–miRNA Associations with Graph Regularization Techniques in Heterogeneous Networks

MicroRNAs (miRNAs) are significant regulators of post-transcriptional levels and have been confirmed to be targeted by small molecule (SM) drugs. It is a novel insight to treat human diseases and accelerate drug discovery by targeting miRNA with small molecules. Computational approaches for discovering novel small molecule–miRNA associations by integrating more heterogeneous network information provide a new idea for the multiple node association prediction between small molecule–miRNA and small molecule–disease associations at a system level. In this study, we proposed a new computational model based on graph regularization techniques in heterogeneous networks, called identification of small molecule–miRNA associations with graph regularization techniques (SMMARTs), to discover potential small molecule–miRNA associations. The novelty of the model lies in the fact that the association score of a small molecule–miRNA pair is calculated by an iterative method in heterogeneous networks that incorporates small molecule–disease associations and miRNA–disease associations. The experimental results indicate that SMMART has better performance than several state-of-the-art methods in inferring small molecule–miRNA associations. Case studies further illustrate the effectiveness of SMMART for small molecule–miRNA association prediction.