A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction

doi:10.1039/c6mb00853d

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction
	Chen, Xing 1; Jiang, Zhi-Chao 2; Xie, Di 3; Huang, De-Shuang 2; Zhao, Qi 3,4; Yan, Gui-Ying5 ; You, Zhu-Hong 6
刊名	MOLECULAR BIOSYSTEMS
	2017-06-01
卷号	13 期号:6 页码:1202-1212
ISSN号	1742-206X
DOI	10.1039/c6mb00853d
英文摘要	In recent years, more and more studies have indicated that microRNAs (miRNAs) play critical roles in various complex human diseases and could be regarded as important biomarkers for cancer detection in early stages. Developing computational models to predict potential miRNA-disease associations has become a research hotspot for significant reduction of experimental time and cost. Considering the various disadvantages of previous computational models, we proposed a novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction (SDMMDA) to predict potential miRNA-disease associations by integrating known associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for diseases and miRNAs. SDMMDA could be applied to new diseases without any known associated miRNAs as well as new miRNAs without any known associated diseases. Due to the fact that there are very few known miRNA-disease associations and many associations are 'missing' in the known training dataset, we introduce the concepts of 'super-miRNA' and 'super-disease' to enhance the similarity measures of diseases and miRNAs. These super classes could help in including the missing associations and improving prediction accuracy. As a result, SDMMDA achieved reliable performance with AUCs of 0.9032, 0.8323, and 0.8970 in global leave-one-out cross validation, local leave-one-out cross validation, and 5-fold cross validation, respectively. In addition, esophageal neoplasms, breast neoplasms, and prostate neoplasms were taken as independent case studies, where 46, 43 and 48 out of the top 50 predicted miRNAs were successfully confirmed by recent experimental literature. It is anticipated that SDMMDA would be an important biological resource for experimental guidance.
资助项目	National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[61133010] ; National Natural Science Foundation of China[61520106006] ; National Natural Science Foundation of China[31571364] ; National Natural Science Foundation of China[61532008] ; National Natural Science Foundation of China[61572364] ; National Natural Science Foundation of China[11371355] ; National Natural Science Foundation of China[61572506] ; Education Department of Liaoning Province[LT2015011] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向	Biochemistry & Molecular Biology
语种	英语
出版者	ROYAL SOC CHEMISTRY
WOS记录号	WOS:000402376500015
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/25608]
专题	应用数学研究所
通讯作者	Chen, Xing; Huang, De-Shuang
作者单位	1.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China 2.Tongji Univ, Sch Elect & Informat Engn, Shanghai 201804, Peoples R China 3.Liaoning Univ, Sch Math, Shenyang 110036, Peoples R China 4.Res Ctr Comp Simulating & Informat Proc Biomacrom, Shenyang 110036, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 6.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
推荐引用方式 GB/T 7714	Chen, Xing,Jiang, Zhi-Chao,Xie, Di,et al. A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction[J]. MOLECULAR BIOSYSTEMS,2017,13(6):1202-1212.
APA	Chen, Xing.,Jiang, Zhi-Chao.,Xie, Di.,Huang, De-Shuang.,Zhao, Qi.,...&You, Zhu-Hong.(2017).A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction.MOLECULAR BIOSYSTEMS,13(6),1202-1212.
MLA	Chen, Xing,et al."A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction".MOLECULAR BIOSYSTEMS 13.6(2017):1202-1212.