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BioMed Research International
Volume 2015 (2015), Article ID 406463, 9 pages
Research Article

A miRNA-Driven Inference Model to Construct Potential Drug-Disease Associations for Drug Repositioning

1School of Software, East China Jiaotong University, Nanchang 330013, China
2Intelligent Optimization & Information Processing Lab, East China Jiaotong University, Nanchang 330013, China
3School of Information Science and Engineering, Central South University, Changsha 410083, China

Received 28 November 2014; Revised 13 January 2015; Accepted 29 January 2015

Academic Editor: Eugenio Ferreira

Copyright © 2015 Hailin Chen and Zuping Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 (, 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.