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 Genetjournal_title
Frontiers in geneticsauthors
Xu J,Cai L,Liao B,Zhu W,Wang P,Meng Y,Lang J,Tian G,Yang Jdoi
10.3389/fgene.2019.01234subject
Has Abstractpub_date
2019-12-11 00:00:00pages
1234issn
1664-8021journal_volume
10pub_type
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