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dc.contributor.authorShen, Z-
dc.contributor.authorZhang, YH-
dc.contributor.authorHan, K-
dc.contributor.authorNandi, AK-
dc.contributor.authorHonig, B-
dc.contributor.authorHuang, DS-
dc.identifier.citationComplexity, 2017, 2017, Article ID 2498957 (9 pp.)en_US
dc.description.abstractCopyright © 2017 Zhen Shen et al. As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.en_US
dc.description.sponsorshipThis work was supported by the grants of the National ScienceFoundation of China, nos. 61520106006, 61732012, 31571364,61532008, U1611265, 61672382, 61772370, 61402334, 61472173,and 61472282, and China Postdoctoral Science Foundation [Grant nos. 2015M580352, 2017M611619, and 2016M601646].en_US
dc.titleMiRNA-disease association prediction with collaborative matrix factorizationen_US
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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