SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction

doi:10.18632/oncotarget.22812

CSpace > 应用数学研究所

	SSCMDA: spy and super cluster strategy for MiRNA-disease association prediction
	Zhao, Qi 1,2; Xie, Di 1; Liu, Hongsheng 2,3; Wang, Fan 4,5; Yan, Gui-Ying6 ; Chen, Xing 7
	2018-01-05
发表期刊	ONCOTARGET
ISSN	1949-2553
卷号	9 期号:2 页码:1826-1842
摘要	In 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.
关键词	microRNA disease association prediction spy strategy super cluster strategy
DOI	10.18632/oncotarget.22812
语种	英语
资助项目	National 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研究方向	Oncology ; Cell Biology
WOS类目	Oncology ; Cell Biology
WOS记录号	WOS:000419623200027
出版者	IMPACT JOURNALS LLC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/29634
专题	应用数学研究所
通讯作者	Zhao, Qi; Chen, Xing
作者单位	1.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
推荐引用方式 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.