CSpace  > 应用数学研究所
MCMDA: Matrix completion for MiRNA-disease association prediction
Li, Jian-Qiang1; Rong, Zhi-Hao2; Chen, Xing3; Yan, Gui-Ying4; You, Zhu-Hong5
Source PublicationONCOTARGET
AbstractNowadays, 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.
KeywordmiRNA disease miRNA-disease association matrix completion
Funding ProjectNational 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 Research AreaOncology ; Cell Biology
WOS SubjectOncology ; Cell Biology
WOS IDWOS:000397642400057
Citation statistics
Document Type期刊论文
Corresponding AuthorChen, Xing
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Jian-Qiang]'s Articles
[Rong, Zhi-Hao]'s Articles
[Chen, Xing]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Jian-Qiang]'s Articles
[Rong, Zhi-Hao]'s Articles
[Chen, Xing]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Jian-Qiang]'s Articles
[Rong, Zhi-Hao]'s Articles
[Chen, Xing]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.