Impact Factor 3.517 | CiteScore 3.60
More on impact ›

Methods ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.01234

Identifying Potential MiRNA-disease Associations with Probability Matrix Factorization

 Junlin Xu1*, Lijun Cai1,  Bo Liao2,  Wen Zhu1,  Peng Wang1,  Yajie Meng1, Jidong Lang3 and  Jialiang Yang2
  • 1College of Computer Science and Electronics Engineering, Hunan University, China
  • 2School of Mathematics and Statistics, Hainan Normal University, China
  • 3Geneis Beijing Co., Ltd., China

In recent years, miRNAs have been verified to play an irreplaceable role in biological processes associated with human disease. Discovering potential disease-related miRNAs helps explain the underlying pathogenesis of the disease at the molecular level. Given the high cost and labor intensity of biological experiments, computational predictions will be an indispensable alternative. Therefore, we design a new model called probability matrix factorization (PMFMDA). Specifically, we first integrate miRNA and disease similarity, Next, the known association matrix and integrated similarity matrix are utilized to construct a probability matrix factorization algorithm to identify potentially relevant miRNAs for disease. We find that PMFMDA achieves reliable performance in the frameworks of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (AUCs are 0.9237 and 0.9187, respectively) in the HMDD (V2.0) dataset, significantly outperforming a few state-of-the-art methods including CMFMDA, IMCMDA, NCPMDA, RLSMDA and RWRMDA. In addition, case studies show that PMFMDA has good predictive performance for new associations, and the evidence can be identified by literature mining.

Keywords: Diseases, miRNAs, Probabilistic matrix factorization, association prediction, Receiver operating characteristic curve (ROC)

Received: 29 May 2019; Accepted: 06 Nov 2019.

Copyright: © 2019 Xu, Cai, Liao, Zhu, Wang, Meng, Lang and Yang. 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(s) 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: Dr. Junlin Xu, College of Computer Science and Electronics Engineering, Hunan University, Changsha, Hunan Province, China,