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The Scientific World Journal
Volume 2013 (2013), Article ID 204658, 6 pages
http://dx.doi.org/10.1155/2013/204658
Research Article

Prediction of Associations between OMIM Diseases and MicroRNAs by Random Walk on OMIM Disease Similarity Network

1School of Information Science and Engineering, Central South University, Changsha 410083, China
2Department of Computer Science and Technology, Hunan University of Humanities, Science and Technology, Loudi 417000, China

Received 28 January 2013; Accepted 19 February 2013

Academic Editors: K. Abdelmohsen and Y. Xi

Copyright © 2013 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.

Abstract

Increasing evidence has revealed that microRNAs (miRNAs) play important roles in the development and progression of human diseases. However, efforts made to uncover OMIM disease-miRNA associations are lacking and the majority of diseases in the OMIM database are not associated with any miRNA. Therefore, there is a strong incentive to develop computational methods to detect potential OMIM disease-miRNA associations. In this paper, random walk on OMIM disease similarity network is applied to predict potential OMIM disease-miRNA associations under the assumption that functionally related miRNAs are often associated with phenotypically similar diseases. Our method makes full use of global disease similarity values. We tested our method on 1226 known OMIM disease-miRNA associations in the framework of leave-one-out cross-validation and achieved an area under the ROC curve of 71.42%. Excellent performance enables us to predict a number of new potential OMIM disease-miRNA associations and the newly predicted associations are publicly released to facilitate future studies. Some predicted associations with high ranks were manually checked and were confirmed from the publicly available databases, which was a strong evidence for the practical relevance of our method.