Abstract
In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.
Highlights
MicroRNAs are an abundant class of small (20~25 nucleotides) endogenous noncoding RNAs, which were normally deemed as negative gene regulators by suppressing the expression of messenger RNAs in a sequence-specific manner and repressing the protein translation of their target genes [1,2,3,4]
Based on the assumption that functionally related miRNAs tend to be involved in phenotypically similar disease and vice versa, the model of Path-Based MiRNA-Disease Association (PBMDA) was developed to prioritize the underlying miRNA-disease associations by adopting a special depth-first search algorithm in a heterogeneous graph, which was composed of known miRNA-disease association network, miRNA similarity network, and disease similarity network
We further implemented the case studies of three important human complex diseases, 88%, 88% and 90% of top-50 predicted miRNA-disease associations have been manually confirmed based on recent experimental reports
Summary
MicroRNAs (miRNAs) are an abundant class of small (20~25 nucleotides) endogenous noncoding RNAs, which were normally deemed as negative gene regulators by suppressing the expression of messenger RNAs (mRNAs) in a sequence-specific manner and repressing the protein translation of their target genes [1,2,3,4]. With the advances in molecular biology and biotechnology, miRNAs have been proven to influence many important physiological processes such as cell growth [9], immune reaction [10], cell differentiation [11], cell development [9], cell cycle regulation [12], inflammation [13], cell apoptosis [14], stress response [9,15], and tumor invasion [16]. MiR-145 was observed to target the insulin receptor substrate-1 and restrain the growth of colon cancer cells [18]. The accumulating miRNA-disease associations could be utilized for the pathological classification, individualized diagnosis, and disease treatment [20]
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