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PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction
You, Zhu-Hong1; Huang, Zhi-An2; Zhu, Zexuan2; Yan, Gui-Ying3; Li, Zheng-Wei4; Wen, Zhenkun2; Chen, Xing5
AbstractIn 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.
Funding ProjectNational Natural Science Foundation of China[11301517] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[61471246] ; National Natural Science Foundation of China[11371355] ; National Natural Science Foundation of China[61572506] ; National Natural Science Foundation of China[61572328] ; Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions[Yq2013141] ; Guangdong Special Support Program of Top-notch Young Professionals[2014TQ01X273] ; China-UK Visual Information Processing Lab
WOS Research AreaBiochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Mathematical & Computational Biology
WOS IDWOS:000398031900018
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Document Type期刊论文
Corresponding AuthorZhu, Zexuan; Chen, Xing
Affiliation1.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi, Peoples R China
2.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
4.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou, Peoples R China
5.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Peoples R China
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You, Zhu-Hong,Huang, Zhi-An,Zhu, Zexuan,et al. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction[J]. PLOS COMPUTATIONAL BIOLOGY,2017,13(3):22.
APA You, Zhu-Hong.,Huang, Zhi-An.,Zhu, Zexuan.,Yan, Gui-Ying.,Li, Zheng-Wei.,...&Chen, Xing.(2017).PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.PLOS COMPUTATIONAL BIOLOGY,13(3),22.
MLA You, Zhu-Hong,et al."PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction".PLOS COMPUTATIONAL BIOLOGY 13.3(2017):22.
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