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.
Highlights
It is known that miRNAs often play an irreplaceable role in biological processes related to human diseases (Shen et al, 2017)
We construct a mathematical model based on probability matrix factorization (PMFMDA) to identifying potential miRNAs–disease associations
PMFMDA uses known correlation data, and integrates the similarities between miRNAs and between diseases. This has enabled PMFMDA to achieve good results in predicting isolated diseaseassociated miRNAs since theoretically similar miRNAs may associate with similar diseases
Summary
It has shown that human disease is associated with abnormal expression of miRNAs, whose analyses can guide the diagnosis, prognosis and treatment of certain diseases (Liang et al, 2019). Due to the growing power of sequencing technology, more and more omics data have been published (Yi et al, 2017), which provides a chance to reveal what role miRNAs play in physiology and pathology. Typical directions include miRNAs–disease interaction prediction, miRNA–miRNA regulatory module discovery, and so on (Chou et al, 2016). All these studies enrich our understanding of the functional regulation mechanisms of miRNA (Ha et al, 2019)
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