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GIMDA: Graphlet interaction-based MiRNA-disease association prediction

文献类型：期刊论文

作者Chen, Xing1; Guan, Na-Na2; Li, Jian-Qiang2; Yan, Gui-Ying3
刊名JOURNAL OF CELLULAR AND MOLECULAR MEDICINE
出版日期2018-03-01
卷号22期号:3页码:1548-1561
关键词miRNA disease miRNA-disease association graphlet interaction
ISSN号1582-4934
DOI10.1111/jcmm.13429
英文摘要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.
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
语种英语
WOS记录号WOS:000426069300016
出版者WILEY
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/29652]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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