Abstract
The crucial roles played by microRNAs (miRNAs) in regulating various biological functions and in disease incidence have been reported continuously over the past decades. Therefore, the identification of novel disease-related miRNAs could help in understanding human disease etiology and pathogenesis further. Due to the involvement of high cost and more time in clinical experiments, development of accurate and feasible computational models is considered highly significant. Here, we aim to present a novel computational model of metric learning for predicting miRNA-disease associations (MLMD). MLMD aims at learning miRNA-disease metric to unravel not only novel disease-related miRNAs but also the miRNA-miRNA and disease-disease similarities. Comprehensive experimental results clearly proved the outstanding performance of MLMD compared to several state-of-the-art methods. MLMD achieved a reliable AUC score of 0.9106 and 0.8786 in the framework of global and local leave-one-out cross validations (LOOCV), respectively. Furthermore, we implemented case studies on two major human cancers (breast cancer and lung cancer) for comparative analysis with already known disease-related miRNAs. Results revealed the top 50 potential candidates were all disease-related miRNAs based on human public databases and literature analysis. We conclude that MLMD could not only serve as practical and feasible framework for inferring potential miRNA-disease associations, but also provide clues for understanding the human complex diseases.
Highlights
MicroRNAs are single-stranded, small noncoding RNAs that play significant roles in regulating mRNA expression by suppressing the translation of target mRNAs [1], [2]
leave-one-out cross validations (LOOCV) can be thought of specific type of n-fold cross validation that each disease-related miRNA can be regarded as the test data while the rest of the entries are used as training data
Despite miRNAs being a significant factor in disease pathogenesis, the number of revealed miRNA-disease associations still remains less because it takes both time and material resources in clinical side
Summary
MicroRNAs (miRNAs) are single-stranded, small noncoding RNAs that play significant roles in regulating mRNA expression by suppressing the translation of target mRNAs [1], [2]. Accumulated evidence has revealed that miRNAs play important role in positive regulation, thereby affecting disease incidence [3], [4]. It is not surprising that identification of the association between miRNAs and diseases is considered as an important issue. More attentions has been increasingly paid to the development of novel computational approaches to elucidate the significant roles of miRNAs in both physiological and pathological conditions [13]
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