Research Papers:

EPMDA: an expression-profile based computational model for microRNA-disease association prediction

Yu-An Huang, Zhu-Hong You, Li-Ping Li, Zhi-An Huang _, Lu-Xuan Xiang, Xiao-Fang Li and Lin-Tao Lv

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Oncotarget. 2017; 8:87033-87043. https://doi.org/10.18632/oncotarget.18788

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Yu-An Huang1,*, Zhu-Hong You1,*, Li-Ping Li2, Zhi-An Huang3, Lu-Xuan Xiang4, Xiao-Fang Li1 and Lin-Tao Lv1

1College of Information Engineering, Xijing University, Xi’an 710123, China

2Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China

3College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

4Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China

*The first two authors should be regarded as joint First Authors

Correspondence to:

Zhu-Hong You, email: [email protected]

Li-Ping Li, email: [email protected]

Keywords: disease, MicroRNA, expression profile, biomarker

Received: April 25, 2017    Accepted: May 29, 2017    Published: June 28, 2017


MicroRNA has become a new star molecule for understanding multiple biological processes and the mechanism of various complex human diseases. Even though a number of computational models have been proposed for predicting the association between microRNAs and various human diseases, most of them are mainly based on microRNA functional similarity and heterogeneous biological networks which suffer from inevitable computational error and bias. In this work, considering the limitation of information resource used by existing methods, we proposed EPMDA model which is the first computational method using the expression profiles of microRNAs to predict the most potential microRNAs associated with various diseases. Based on the dataset constructed from HMDD v2.0 database, EPMDA obtained AUCs of 0.8945 and 0.8917 based on the leave-one-out and 5-fold cross validation, respectively. Furthermore, EPMDA was applied to two important human diseases. As a result, 80% and 88% microRNAs in the top-25 lists of Colon Neoplasms and Kidney Neoplasms were confirmed by other databases. The performance comparison of EPMDA with existing prediction models and classical algorithms also demonstrated the reliable prediction ability of EPMDA. It is anticipated that EPMDA can be used as an effective computational tool for future biomedical researches.

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