Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/19490
Title: MiRNA-disease association prediction with collaborative matrix factorization
Authors: Shen, Z
Zhang, YH
Han, K
Nandi, AK
Honig, B
Huang, DS
Issue Date: 28-Sep-2017
Publisher: Hindawi
Citation: Complexity, 2017, 2017, Article ID 2498957 (9 pp.)
Abstract: Copyright © 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.
URI: https://bura.brunel.ac.uk/handle/2438/19490
DOI: https://dx.doi.org/10.1155/2017/2498957
ISSN: 1076-2787
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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