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Sci Rep. 2016 Feb 16;6:21106. doi: 10.1038/srep21106.

WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.

Author information

1
National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China.
2
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
3
Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, 310018, China.
4
Department of Automation, Tsinghua University, Beijing, 100084, China.
5
School of Mechanical, Electrical &Information Engineering, Shandong University, Weihai, 264209, China.
6
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.
7
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
8
University of Chinese Academy of Sciences, Beijing, 100049, China.
9
School of Economics and Management, Beihang University, Beijing, 100191, China.
10
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.

Abstract

Increasing evidences have indicated that microRNAs (miRNAs) are functionally associated with the development and progression of various complex human diseases. However, the roles of miRNAs in multiple biological processes or various diseases and their underlying molecular mechanisms still have not been fully understood yet. Predicting potential miRNA-disease associations by integrating various heterogeneous biological datasets is of great significance to the biomedical research. Computational methods could obtain potential miRNA-disease associations in a short time, which significantly reduce the experimental time and cost. Considering the limitations in previous computational methods, we developed the model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) to predict potential miRNAs associated with various complex diseases. WBSMDA could be applied to the diseases without any known related miRNAs. The AUC of 0.8031 based on Leave-one-out cross validation has demonstrated its reliable performance. WBSMDA was further applied to Colon Neoplasms, Prostate Neoplasms, and Lymphoma for the identification of their potential related miRNAs. As a result, 90%, 84%, and 80% of predicted miRNA-disease pairs in the top 50 prediction list for these three diseases have been confirmed by recent experimental literatures, respectively. It is anticipated that WBSMDA would be a useful resource for potential miRNA-disease association identification.

PMID:
26880032
PMCID:
PMC4754743
DOI:
10.1038/srep21106
[Indexed for MEDLINE]
Free PMC Article

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