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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
2017-12-12
Source PublicationJOURNAL OF TRANSLATIONAL MEDICINE
ISSN1479-5876
Volume15Pages:14
AbstractBackground: 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.
KeywordmiRNA Disease miRNA-disease association Multiple kernel learning Kronecker regularized least squares
DOI10.1186/s12967-017-1340-3
Language英语
Funding ProjectNational 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 AreaResearch & Experimental Medicine
WOS SubjectMedicine, Research & Experimental
WOS IDWOS:000418147200002
PublisherBIOMED CENTRAL LTD
Citation statistics
Cited Times:22[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/27053
Collection应用数学研究所
Affiliation1.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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