PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction

• Published in: PLoS computational biology 2017-03, Vol.13 (3), p.e1005455-e1005455
• Main Authors: You, Zhu-Hong, Huang, Zhi-An, Zhu, Zexuan, Yan, Gui-Ying, Li, Zheng-Wei, Wen, Zhenkun, Chen, Xing
• Format: Article
• Language: English
• Subjects: Biological activity; Biological computing; Biology and life sciences; Biomarkers, Tumor - genetics; Case studies; Cell cycle; Colon; Colorectal cancer; Computer applications; Computer science; Computer Simulation; Disease; Diseases; Drugs; Esophagus; Genetic Association Studies; Genetic Predisposition to Disease - epidemiology; Genetic Predisposition to Disease - genetics; Health aspects; Humans; Kidney cancer; Kidneys; Lupus; Medical prognosis; Medical research; Medicine and Health Sciences; Methods; MicroRNA; MicroRNAs; MicroRNAs - genetics; miRNA; Models, Genetic; Models, Statistical; Molecular modelling; Neoplasms; Neoplasms - epidemiology; Neoplasms - genetics; Pathogenesis; Physical Sciences; Prediction models; Predictions; Prevalence; Prognosis; Proteins; Research and Analysis Methods; Risk Assessment - methods; Risk Factors; RNA sequencing; Search algorithms; Signal Transduction - genetics; Similarity; Software; Software engineering; Thoracic surgery; Tumors; Urology
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005455

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.