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...
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| Published in | PLoS computational biology Vol. 13; no. 3; p. e1005455 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.03.2017
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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.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. 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. 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, P ath- B ased M iRNA- D isease A ssociation (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. Identification of miRNA-disease associations is considered as a key way for the development of pathology, diagnose and therapy. Computational prediction models contribute to discovering the underlying disease-related miRNAs on a large scale. Based on the assumption that functionally related miRNAs tend to be involved in phenotypically similar disease and vice versa, the model of 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. Through leave-one-out cross validation and 5-fold cross validation, the promising results demonstrated the effectiveness of the proposed model. 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. It is anticipated that PBMDA could prioritize the most potential miRNA-disease associations on a large scale for advancing the progress of biological experiment validation in the future, which could further contribute to the understanding of complex disease mechanisms. |
| Audience | Academic |
| Author | Huang, Zhi-An You, Zhu-Hong Wen, Zhenkun Chen, Xing Yan, Gui-Ying Li, Zheng-Wei Zhu, Zexuan |
| AuthorAffiliation | 2 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China 3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China 5 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China 1 Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, ürümqi, China 4 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China University of Calgary Cumming School of Medicine, CANADA |
| AuthorAffiliation_xml | – name: University of Calgary Cumming School of Medicine, CANADA – name: 4 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China – name: 2 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China – name: 3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China – name: 5 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – name: 1 Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, ürümqi, China |
| Author_xml | – sequence: 1 givenname: Zhu-Hong surname: You fullname: You, Zhu-Hong – sequence: 2 givenname: Zhi-An orcidid: 0000-0001-9974-148X surname: Huang fullname: Huang, Zhi-An – sequence: 3 givenname: Zexuan surname: Zhu fullname: Zhu, Zexuan – sequence: 4 givenname: Gui-Ying surname: Yan fullname: Yan, Gui-Ying – sequence: 5 givenname: Zheng-Wei surname: Li fullname: Li, Zheng-Wei – sequence: 6 givenname: Zhenkun surname: Wen fullname: Wen, Zhenkun – sequence: 7 givenname: Xing orcidid: 0000-0001-9028-5342 surname: Chen fullname: Chen, Xing |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28339468$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2017 Public Library of Science 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: You Z-H, Huang Z-A, Zhu Z, Yan G-Y, Li Z-W, Wen Z, et al. (2017) PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLoS Comput Biol 13(3): e1005455. https://doi.org/10.1371/journal.pcbi.1005455 2017 You et al 2017 You et al 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: You Z-H, Huang Z-A, Zhu Z, Yan G-Y, Li Z-W, Wen Z, et al. (2017) PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLoS Comput Biol 13(3): e1005455. https://doi.org/10.1371/journal.pcbi.1005455 |
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| Notes | new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceptualization: XC.Data curation: XC ZAH.Formal analysis: ZHY XC GYY.Funding acquisition: XC ZZ GYY ZHY ZW.Investigation: XC GYY ZWL.Methodology: XC.Project administration: XC ZZ.Resources: XC.Software: ZAH XC.Supervision: XC ZZ.Validation: XC ZAH ZHY.Visualization: ZAH XC ZHY ZZ GYY ZWL.Writing – original draft: ZAH XC ZHY.Writing – review & editing: XC ZAH ZZ. These authors are joint senior authors on this work. The authors have declared that no competing interests exist. |
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| SubjectTerms | 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 Esophageal cancer 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 |
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| Title | PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction |
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