KMS Of Academy of mathematics and systems sciences, CAS
| Integrating random walk and binary regression to identify novel miRNA-disease association | |
Niu, Ya-Wei1; Wang, Guang-Hui1; Yan, Gui-Ying2 ; Chen, Xing3
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| 2019-01-28 | |
| Source Publication | BMC BIOINFORMATICS
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| ISSN | 1471-2105 |
| Volume | 20Pages:13 |
| Abstract | BackgroundIn the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly.ResultsIn this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database.ConclusionsWe anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works. |
| Keyword | microRNA Disease miRNA-disease association Random walk Binary regression |
| DOI | 10.1186/s12859-019-2640-9 |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61772531] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[11471193] ; Foundation for Distinguished Young Scholars of Shandong Province[JQ201501] ; Qilu Scholar Award of Shandong University |
| WOS Research Area | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
| WOS Subject | Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
| WOS ID | WOS:000456922800002 |
| Publisher | BMC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/31815 |
| Collection | 应用数学研究所 |
| Corresponding Author | Wang, Guang-Hui; Chen, Xing |
| Affiliation | 1.Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.China Univ Min & Technol, Sch Informat & Control Engn, 1 Daxue Rd, Xuzhou 221116, Jiangsu, Peoples R China |
| Recommended Citation GB/T 7714 | Niu, Ya-Wei,Wang, Guang-Hui,Yan, Gui-Ying,et al. Integrating random walk and binary regression to identify novel miRNA-disease association[J]. BMC BIOINFORMATICS,2019,20:13. |
| APA | Niu, Ya-Wei,Wang, Guang-Hui,Yan, Gui-Ying,&Chen, Xing.(2019).Integrating random walk and binary regression to identify novel miRNA-disease association.BMC BIOINFORMATICS,20,13. |
| MLA | Niu, Ya-Wei,et al."Integrating random walk and binary regression to identify novel miRNA-disease association".BMC BIOINFORMATICS 20(2019):13. |
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