RBMMMDA: predicting multiple types of disease-microRNA associations

doi:10.1038/srep13877

CSpace

	RBMMMDA: predicting multiple types of disease-microRNA associations
	Chen, Xing 1,2; Yan, Chenggang Clarence 3; Zhang, Xiaotian 4; Li, Zhaohui 5,6; Deng, Lixi 7,8; Zhang, Yongdong 9; Dai, Qionghai 3
	2015-09-08
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
卷号	5 页码:13
摘要	Accumulating evidences have shown that plenty of miRNAs play fundamental and important roles in various biological processes and the deregulations of miRNAs are associated with a broad range of human diseases. However, the mechanisms underlying the dysregulations of miRNAs still have not been fully understood yet. All the previous computational approaches can only predict binary associations between diseases and miRNAs. Predicting multiple types of disease-miRNA associations can further broaden our understanding about the molecular basis of diseases in the level of miRNAs. In this study, the model of Restricted Boltzmann machine for multiple types of miRNA-disease association prediction (RBMMMDA) was developed to predict four different types of miRNA-disease associations. Based on this model, we could obtain not only new miRNA-disease associations, but also corresponding association types. To our knowledge, RBMMMDA is the first model which could computationally infer association types of miRNA-disease pairs. Leave-one-out cross validation was implemented for RBMMMDA and the AUC of 0.8606 demonstrated the reliable and effective performance of RBMMMDA. In the case studies about lung cancer, breast cancer, and global prediction for all the diseases simultaneously, 50, 42, and 45 out of top 100 predicted miRNA-disease association types were confirmed by recent biological experimental literatures, respectively.
DOI	10.1038/srep13877
语种	英语
资助项目	National Natural Science of Foundation of China[11301517] ; National Natural Science of Foundation of China[61472203] ; National Natural Science of Foundation of China[61327902] ; National Center for Mathematics and Interdisciplinary Sciences, CAS ; State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000360799600003
出版者	NATURE PUBLISHING GROUP
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/20719
专题	中国科学院数学与系统科学研究院
通讯作者	Chen, Xing
作者单位	1.Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 4.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China 5.Tsinghua Univ, Sch Life Sci, Beijing 100084, Peoples R China 6.Natl Inst Biol Sci, Beijing 102206, Peoples R China 7.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 8.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 9.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chen, Xing,Yan, Chenggang Clarence,Zhang, Xiaotian,et al. RBMMMDA: predicting multiple types of disease-microRNA associations[J]. SCIENTIFIC REPORTS,2015,5:13.
APA	Chen, Xing.,Yan, Chenggang Clarence.,Zhang, Xiaotian.,Li, Zhaohui.,Deng, Lixi.,...&Dai, Qionghai.(2015).RBMMMDA: predicting multiple types of disease-microRNA associations.SCIENTIFIC REPORTS,5,13.
MLA	Chen, Xing,et al."RBMMMDA: predicting multiple types of disease-microRNA associations".SCIENTIFIC REPORTS 5(2015):13.