miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Author:

Shen Zhen1,Zhang You-Hua2,Han Kyungsook3,Nandi Asoke K.4,Honig Barry5,Huang De-Shuang1ORCID

Affiliation:

1. Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China

2. School of Information and Computer, Anhui Agricultural University, Changjiang West Road 130, Hefei, Anhui, China

3. Department of Computer Science and Engineering, Inha University, Incheon, Republic of Korea

4. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK

5. Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, USA

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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