PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
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.
Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, ürümqi, China.
Zhu-Hong
ZH
You
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Zhi-An
0000-0001-9974-148X
ZA
Huang
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Zexuan
Z
Zhu
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
Gui-Ying
GY
Yan
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
Zheng-Wei
ZW
Li
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Zhenkun
Z
Wen
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
Xing
0000-0001-9028-5342
X
Chen
D014408:Biomarkers, Tumor / Q000235:genetics*; D003198:Computer Simulation; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology* / Q000235:genetics*; D015995:Prevalence; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D015398:Signal Transduction / Q000235:genetics
D014408:Biomarkers, Tumor; D035683:MicroRNAs