Research Papers:

MCMDA: Matrix completion for MiRNA-disease association prediction

Jian-Qiang Li, Zhi-Hao Rong, Xing Chen _, Gui-Ying Yan and Zhu-Hong You

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Oncotarget. 2017; 8:21187-21199. https://doi.org/10.18632/oncotarget.15061

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Jian-Qiang Li1,*, Zhi-Hao Rong2,*, Xing Chen3, Gui-Ying Yan4, Zhu-Hong You5

1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

2School of Software, Beihang University, Beijing, 100191, China

3School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China

4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

5Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, ÜRÜMQI, 830011, China

*The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint first authors

Correspondence to:

Xing Chen, email: [email protected]

Keywords: miRNA, disease, miRNA-disease association, matrix completion

Received: October 17, 2016     Accepted: January 09, 2017     Published: February 03, 2017


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-one-out 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.

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