Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization.

Abstract:

: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.

journal_name

Front Genet

journal_title

Frontiers in genetics

authors

Xu J,Cai L,Liao B,Zhu W,Wang P,Meng Y,Lang J,Tian G,Yang J

doi

10.3389/fgene.2019.01234

subject

Has Abstract

pub_date

2019-12-11 00:00:00

pages

1234

issn

1664-8021

journal_volume

10

pub_type

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