A miRNA-driven inference model to construct potential drug-disease associations for drug repositioning

Increasing evidence discovered that the inappropriate expression of microRNAs (miRNAs) will lead to many kinds of complex diseases and drugs can regulate the expression level of miRNAs. Therefore human diseases may be treated by targeting some specific miRNAs with drugs, which provides a new perspective for drug repositioning. However, few studies have attempted to computationally predict associations between drugs and diseases via miRNAs for drug repositioning. In this paper, we developed an inference model to achieve this aim by combining experimentally supported drug-miRNA associations and miRNA-disease associations with the assumption that drugs will form associations with diseases when they share some significant miRNA partners. Experimental results showed excellent performance of our model. Case studies demonstrated that some of the strongly predicted drug-disease associations can be confirmed by the publicly accessible database CTD (www.ctdbase.org), which indicated the usefulness of our inference model. Moreover, candidate miRNAs as molecular hypotheses underpinning the associations were listed to guide future experiments. The predicted results were released for further studies. We expect that this study will provide help in our understanding of drug-disease association prediction and in the roles of miRNAs in drug repositioning.