MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA–disease association prediction

doi:10.1186/s12967-017-1340-3

CSpace > 应用数学研究所

	MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA–disease association prediction
	Chen,Xing 1; Niu,Ya-Wei 2; Wang,Guang-Hui 2; Yan,Gui-Ying3
	2017-12-12
发表期刊	Journal of Translational Medicine
ISSN	1479-5876
卷号	15 期号:1
摘要	AbstractBackgroundRecently, 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.MethodsIn 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.ResultsMKRMDA 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.ConclusionsAll 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.
关键词	miRNA Disease miRNA–disease association Multiple kernel learning Kronecker regularized least squares
DOI	10.1186/s12967-017-1340-3
语种	英语
WOS记录号	BMC:10.1186/s12967-017-1340-3
出版者	BioMed Central
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/399
专题	应用数学研究所
通讯作者	Chen,Xing; Wang,Guang-Hui
作者单位	1. 2. 3.
推荐引用方式 GB/T 7714	Chen,Xing,Niu,Ya-Wei,Wang,Guang-Hui,等. MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA–disease association prediction[J]. Journal of Translational Medicine,2017,15(1).
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(1).
MLA	Chen,Xing,et al."MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA–disease association prediction".Journal of Translational Medicine 15.1(2017).