KMS Of Academy of mathematics and systems sciences, CAS
| MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction | |
Chen, Xing1; Niu, Ya-Wei2; Wang, Guang-Hui2; Yan, Gui-Ying3
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| 2017-12-12 | |
| Source Publication | JOURNAL OF TRANSLATIONAL MEDICINE
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| ISSN | 1479-5876 |
| Volume | 15Pages:14 |
| Abstract | Background: Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. Methods: In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. Results: MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 +/- 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. Conclusions: All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers. |
| Keyword | miRNA Disease miRNA-disease association Multiple kernel learning Kronecker regularized least squares |
| DOI | 10.1186/s12967-017-1340-3 |
| 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] ; National Natural Science Foundation of China[11371355] ; Foundation for Distinguished Young Scholars of Shandong Province[JQ201501] ; Shandong University ; Independent Innovation Foundation of Shandong University |
| WOS Research Area | Research & Experimental Medicine |
| WOS Subject | Medicine, Research & Experimental |
| WOS ID | WOS:000418147200002 |
| Publisher | BIOMED CENTRAL LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/27053 |
| Collection | 应用数学研究所 |
| Corresponding Author | Chen, Xing; Wang, Guang-Hui |
| Affiliation | 1.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China 2.Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Chen, Xing,Niu, Ya-Wei,Wang, Guang-Hui,et al. MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction[J]. JOURNAL OF TRANSLATIONAL MEDICINE,2017,15:14. |
| APA | Chen, Xing,Niu, Ya-Wei,Wang, Guang-Hui,&Yan, Gui-Ying.(2017).MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.JOURNAL OF TRANSLATIONAL MEDICINE,15,14. |
| MLA | Chen, Xing,et al."MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction".JOURNAL OF TRANSLATIONAL MEDICINE 15(2017):14. |
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