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About the Authors:
Zhu-Hong You
¶‡ These authors are joint senior authors on this work.
Affiliation: Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, ürümqi, China
Zhi-An Huang
¶‡ These authors are joint senior authors on this work.
Affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
ORCID http://orcid.org/0000-0001-9974-148X
Zexuan Zhu
* E-mail: [email protected] (XC); [email protected] (ZZ)
Affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Gui-Ying Yan
Affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Zheng-Wei Li
Affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
Zhenkun Wen
Affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Xing Chen
* E-mail: [email protected] (XC); [email protected] (ZZ)
Affiliation: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaAbstract
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...