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SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction
Zhao, Qi1,2; Xie, Di1; Liu, Hongsheng2,3; Wang, Fan4,5; Yan, Gui-Ying6; Chen, Xing7
2018-01-05
Source PublicationONCOTARGET
ISSN1949-2553
Volume9Issue:2Pages:1826-1842
AbstractIn the biological field, the identification of the associations between microRNAs (miRNAs) and diseases has been paid increasing attention as an extremely meaningful study for the clinical medicine. However, it is expensive and time-consuming to confirm miRNA-disease associations by experimental methods. Therefore, in recent years, several effective computational models for predicting the potential miRNA-disease associations have been developed. In this paper, we proposed the Spy and Super Cluster strategy for MiRNA-Disease Association prediction (SSCMDA) based on known miRNA-disease associations, integrated disease similarity and integrated miRNA similarity. For problems of mixed unknown miRNA-disease pairs containing both potential associations and real negative associations, which will lead to inaccurate prediction, spy strategy is adopted by SSCMDA to identify reliable negative samples from the unknown miRNA-disease pairs. Moreover, the super-cluster strategy could gather as many positive samples as possible to improve the accuracy of the prediction by overcoming the shortage of lacking sufficient positive training samples. As a result, the AUCs of global leave-one-out cross validation (LOOCV), local LOOCV and 5-fold cross validation were 0.9007, 0.8747 and 0.8806+/-0.0025, respectively. According to the AUC results, SSCMDA has shown a significant improvement compared with some previous models. We further carried out case studies based on various version of HMDD database to test the prediction performance robustness of SSCMDA. We also implemented case study to examine whether SSCMDA was effective for new diseases without any known associated miRNAs. As a result, a large proportion of the predicted miRNAs have been verified by experimental reports.
KeywordmicroRNA disease association prediction spy strategy super cluster strategy
DOI10.18632/oncotarget.22812
Language英语
Funding ProjectNational Natural Science Foundation of China[61772531] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[31570160] ; National Natural Science Foundation of China[11371355] ; Education Department of Liaoning Province[LT2015011] ; Doctor Startup Foundation from Liaoning Province[20170520217] ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; annual general university graduate research and innovation program of Jiangsu Province, China[KYLX16_0526]
WOS Research AreaOncology ; Cell Biology
WOS SubjectOncology ; Cell Biology
WOS IDWOS:000419623200027
PublisherIMPACT JOURNALS LLC
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29634
Collection应用数学研究所
Affiliation1.Liaoning Univ, Sch Math, Shenyang, Liaoning, Peoples R China
2.Res Ctr Comp Simulating & Informat Proc Biomacrom, Shenyang, Liaoning, Peoples R China
3.Liaoning Univ, Sch Life Sci, Shenyang, Liaoning, Peoples R China
4.China Univ Min & Technol, Sch Mechatron Engn, Xuzhou, Peoples R China
5.China Univ Min & Technol, Jiangsu Key Lab Mine Mech & Elect Equipment, Xuzhou, Peoples R China
6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
7.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Qi,Xie, Di,Liu, Hongsheng,et al. SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction[J]. ONCOTARGET,2018,9(2):1826-1842.
APA Zhao, Qi,Xie, Di,Liu, Hongsheng,Wang, Fan,Yan, Gui-Ying,&Chen, Xing.(2018).SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction.ONCOTARGET,9(2),1826-1842.
MLA Zhao, Qi,et al."SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction".ONCOTARGET 9.2(2018):1826-1842.
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