Oncotarget

Research Papers:

PRMDA: personalized recommendation-based MiRNA-disease association prediction

Zhu-Hong You, Luo-Pin Wang, Xing Chen _, Shanwen Zhang, Xiao-Fang Li, Gui-Ying Yan and Zheng-Wei Li

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Oncotarget. 2017; 8:85568-85583. https://doi.org/10.18632/oncotarget.20996

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Abstract

Zhu-Hong You1,*, Luo-Pin Wang2,*, Xing Chen3, Shanwen Zhang1, Xiao-Fang Li1, Gui-Ying Yan4 and Zheng-Wei Li5

1Department of Information Engineering, Xijing University, Xi’an, China

2International Software School, Wuhan University, Wuhan, China

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

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

5School of Computer Science and Technology, Hefei, China

*Joint first authors

Correspondence to:

Xing Chen, email: [email protected]

Keywords: miRNA, disease, miRNA-disease association, personalized recommendation

Received: February 15, 2017     Accepted: August 29, 2017     Published: September 18, 2017

ABSTRACT

Recently, researchers have been increasingly focusing on microRNAs (miRNAs) with accumulating evidence indicating that miRNAs serve as a vital role in various biological processes and dysfunctions of miRNAs are closely related with human complex diseases. Predicting potential associations between miRNAs and diseases is attached considerable significance in the domains of biology, medicine, and bioinformatics. In this study, we developed a computational model of Personalized Recommendation-based MiRNA-Disease Association prediction (PRMDA) to predict potential related miRNA for all diseases by implementing personalized recommendation-based algorithm based on integrated similarity for diseases and miRNAs. PRMDA is a global method capable of prioritizing candidate miRNAs for all diseases simultaneously. Moreover, the model could be applied to diseases without any known associated miRNAs. PRMDA obtained AUC of 0.8315 based on leave-one-out cross validation, which demonstrated that PRMDA could be regarded as a reliable tool for miRNA-disease association prediction. Besides, we implemented PRMDA on the HMDD V1.0 and HMDD V2.0 databases for three kinds of case studies about five important human cancers in order to test the performance of the model from different perspectives. As a result, 92%, 94%, 88%, 96% and 88% out of the top 50 candidate miRNAs predicted by PRMDA for Colon Neoplasms, Esophageal Neoplasms, Lymphoma, Lung Neoplasms and Breast Neoplasms, respectively, were confirmed by experimental reports.


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PII: 20996