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Vol 52(2018) N 5 p. 749-760; DOI 10.1134/S0026893318050151 Full Text

X.Y. Yang1, L. Gao1*, С. Liang1**

Inferring Disease-miRNA Associations by Self-Weighting with Multiple Data Source

1School of Information Science and Engineering, Shandong Normal University, Jinan, 250358 China

*gaoling825@163.com
**alcs417@sdnu.edu.cn
Received - 2017-06-28; Accepted - 2017-10-05

Increasing evidence has suggested that microRNAs (miRNAs) may function as positive regulators at the post-transcriptional level. A search for associations between miRNAs and diseases is crucial for understanding the pathogenesis. Various publicly available databases have been constructed to store meaningful information on a large number of miRNA molecules. In this study, to resolve the limitation that individual sources of miRNA target data tend to be incomplete and noisy, we propose a network-based computational method called self-weighting for integrating multiple data sources. A bipartite phenotype-miRNA network (BPMN) incorporates known disease-miRNA interactions as well as the similarities between disease phenotypes and functional similarities of miRNAs. Random walk with restart algorithm was deployed on the bipartite network to predict novel disease-miRNA associations. In leave-one-out cross-validation experiments, our technique achieves an AUC of 0.801 when evaluating against known disease-related miRNAs from HMDD. Systematic prioritization of miRNAs for 11 common diseases obtained an average AUC of 0.765. Additionally, a case study on colon cancer uncovered a number of potential miRNA candidates as biomarkers of this disease.

Bipartite network, database, disease-miRNA associations, random walk, self-weighting



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