MCMDA: Matrix completion for MiRNA-disease association prediction

doi:10.18632/oncotarget.15061

CSpace > 应用数学研究所

	MCMDA: Matrix completion for MiRNA-disease association prediction
	Li, Jian-Qiang 1; Rong, Zhi-Hao 2; Chen, Xing 3; Yan, Gui-Ying4 ; You, Zhu-Hong 5
	2017
发表期刊	ONCOTARGET
ISSN	1949-2553
卷号	8 期号:13 页码:21187-21199
摘要	Nowadays, researchers have realized that microRNAs (miRNAs) are playing a significant role in many important biological processes and they are closely connected with various complex human diseases. However, since there are too many possible miRNA-disease associations to analyze, it remains difficult to predict the potential miRNAs related to human diseases without a systematic and effective method. In this study, we developed a Matrix Completion for MiRNA-Disease Association prediction model (MCMDA) based on the known miRNA-disease associations in HMDD database. MCMDA model utilized the matrix completion algorithm to update the adjacency matrix of known miRNA-disease associations and furthermore predict the potential associations. To evaluate the performance of MCMDA, we performed leave-oneout cross validation (LOOCV) and 5-fold cross validation to compare MCMDA with three previous classical computational models (RLSMDA, HDMP, and WBSMDA). As a result, MCMDA achieved AUCs of 0.8749 in global LOOCV, 0.7718 in local LOOCV and average AUC of 0.8767+/-0.0011 in 5-fold cross validation. Moreover, the prediction results associated with colon neoplasms, kidney neoplasms, lymphoma and prostate neoplasms were verified. As a consequence, 84%, 86%, 78% and 90% of the top 50 potential miRNAs for these four diseases were respectively confirmed by recent experimental discoveries. Therefore, MCMDA model is superior to the previous models in that it improves the prediction performance although it only depends on the known miRNA-disease associations.
关键词	miRNA disease miRNA-disease association matrix completion
DOI	10.18632/oncotarget.15061
语种	英语
资助项目	National Natural Science Foundation of China[61572330] ; National Natural Science Foundation of China[11301517] ; National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[11371355] ; National Natural Science Foundation of China[61572506] ; Natural Science foundation of Guangdong Province[2014A030313554] ; Technology Planning Project from Guangdong Province[2014B010118005] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities[2014YC07]
WOS研究方向	Oncology ; Cell Biology
WOS类目	Oncology ; Cell Biology
WOS记录号	WOS:000397642400057
出版者	IMPACT JOURNALS LLC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/24995
专题	应用数学研究所
通讯作者	Chen, Xing
作者单位	1.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China 2.Beihang Univ, Sch Software, Beijing 100191, Peoples R China 3.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
推荐引用方式 GB/T 7714	Li, Jian-Qiang,Rong, Zhi-Hao,Chen, Xing,et al. MCMDA: Matrix completion for MiRNA-disease association prediction[J]. ONCOTARGET,2017,8(13):21187-21199.
APA	Li, Jian-Qiang,Rong, Zhi-Hao,Chen, Xing,Yan, Gui-Ying,&You, Zhu-Hong.(2017).MCMDA: Matrix completion for MiRNA-disease association prediction.ONCOTARGET,8(13),21187-21199.
MLA	Li, Jian-Qiang,et al."MCMDA: Matrix completion for MiRNA-disease association prediction".ONCOTARGET 8.13(2017):21187-21199.