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Front. Physiol. | doi: 10.3389/fphys.2018.00092

GRMDA: Graph Regression for MiRNA-Disease Association prediction

 Xing Chen1*, Jing-Ru Yang2,  Na-Na Guan3 and Jian-Qiang Li3
  • 1School of Information and Control Engineering, China University of Mining and Technology, China
  • 2School of Computer Science and Technology,, Nankai University, China
  • 3College of Computer Science and Software Engineering, Shenzhen University, China

Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and 5-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080+/-0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.

Keywords: microRNA, Disease, association prediction, graph regression, Matrix Factorization

Received: 07 Oct 2017; Accepted: 26 Jan 2018.

Edited by:

Jiarui Wu, Shanghai Institutes for Biological Sciences (CAS), China

Reviewed by:

Alessandro Giuliani, Istituto Superiore di Sanità, Italy
Haoran Zheng, University of Science and Technology of China, China  

Copyright: © 2018 Chen, Yang, Guan and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Xing Chen, China University of Mining and Technology, School of Information and Control Engineering, No.1,Daxue Road,Xuzhou,Jiangsu,221116,P.R.China, Xuzhou, 221116, Jiangsu, China,