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GIMDA: Graphlet interaction-based MiRNA-disease association prediction
Chen, Xing1; Guan, Na-Na2; Li, Jian-Qiang2; Yan, Gui-Ying3
2018-03-01
Source PublicationJOURNAL OF CELLULAR AND MOLECULAR MEDICINE
ISSN1582-4934
Volume22Issue:3Pages:1548-1561
AbstractMicroRNAs (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.
KeywordmiRNA disease miRNA-disease association graphlet interaction
DOI10.1111/jcmm.13429
Language英语
Funding ProjectNational 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 Research AreaCell Biology ; Research & Experimental Medicine
WOS SubjectCell Biology ; Medicine, Research & Experimental
WOS IDWOS:000426069300016
PublisherWILEY
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29653
Collection应用数学研究所
Affiliation1.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
Recommended Citation
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.
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