GIMDA: Graphlet interaction-based MiRNA-disease association prediction

doi:10.1111/jcmm.13429

CSpace > 应用数学研究所

	GIMDA: Graphlet interaction-based MiRNA-disease association prediction
	Chen, Xing 1; Guan, Na-Na 2; Li, Jian-Qiang 2; Yan, Gui-Ying3
	2018-03-01
发表期刊	JOURNAL OF CELLULAR AND MOLECULAR MEDICINE
ISSN	1582-4934
卷号	22 期号:3 页码:1548-1561
摘要	MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 +/- 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2D-isease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.
关键词	miRNA disease miRNA-disease association graphlet interaction
DOI	10.1111/jcmm.13429
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[61572330] ; National Natural Science Foundation of China[11371355] ; Natural Science foundation of Guangdong Province[2014A030313554] ; Technology Planning Project from Guangdong Province[2014B010118005]
WOS研究方向	Cell Biology ; Research & Experimental Medicine
WOS类目	Cell Biology ; Medicine, Research & Experimental
WOS记录号	WOS:000426069300016
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/29653
专题	应用数学研究所
通讯作者	Chen, Xing; Li, Jian-Qiang
作者单位	1.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Peoples R China 2.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chen, Xing,Guan, Na-Na,Li, Jian-Qiang,et al. GIMDA: Graphlet interaction-based MiRNA-disease association prediction[J]. JOURNAL OF CELLULAR AND MOLECULAR MEDICINE,2018,22(3):1548-1561.
APA	Chen, Xing,Guan, Na-Na,Li, Jian-Qiang,&Yan, Gui-Ying.(2018).GIMDA: Graphlet interaction-based MiRNA-disease association prediction.JOURNAL OF CELLULAR AND MOLECULAR MEDICINE,22(3),1548-1561.
MLA	Chen, Xing,et al."GIMDA: Graphlet interaction-based MiRNA-disease association prediction".JOURNAL OF CELLULAR AND MOLECULAR MEDICINE 22.3(2018):1548-1561.