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BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction
Chen, X (Chen, Xing)[ 1 ]; Xie, D (Xie, Di)[ 2 ]; Wang, L (Wang, Lei)[ 1 ]; Zhao, Q (Zhao, Qi)[ 2,3 ]; You, ZH (You, Zhu-Hong)[ 4 ]; Liu, HS (Liu, Hongsheng)[ 3,5 ]
2018
Source PublicationBIOINFORMATICS
ISSN1367-4803
Volume34Issue:18Pages:3178-3186
Abstract

Motivation: A large number of resources have been devoted to exploring the associations between microRNAs (miRNAs) and diseases in the recent years. However, the experimental methods are expensive and time-consuming. Therefore, the computational methods to predict potential miRNA-disease associations have been paid increasing attention. Results: In this paper, we proposed a novel computational model of Bipartite Network Projection for MiRNA-Disease Association prediction (BNPMDA) based on the known miRNA-disease associations, integrated miRNA similarity and integrated disease similarity. We firstly described the preference degree of a miRNA for its related disease and the preference degree of a disease for its related miRNA with the bias ratings. We constructed bias ratings formiRNAs and diseases by using agglomerative hierarchical clustering according to the three types of networks. Then, we implemented the bipartite network recommendation algorithm to predict the potential miRNA-disease associations by assigning transfer weights to resource allocation links between miRNAs and diseases based on the bias ratings. BNPMDA had been shown to improve the prediction accuracy in comparison with previous models according to the area under the receiver operating characteristics (ROC) curve (AUC) results of three typical cross validations. As a result, the AUCs of Global LOOCV, Local LOOCV and 5-fold cross validation obtained by implementing BNPMDA were 0.9028, 0.8380 and 0.8980 6 0.0013, respectively. We further implemented two types of case studies on several important human complex diseases to confirm the effectiveness of BNPMDA. In conclusion, BNPMDA could effectively predict the potential miRNA-disease associations at a high accuracy level.

DOI10.1093/bioinformatics/bty333
Indexed BySCI
WOS IDWOS:000446433800016
Citation statistics
Cited Times:50[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5594
Collection多语种信息技术研究室
Affiliation1.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China
2.Liaoning Univ, Sch Math, Shenyang 110036, Liaoning, Peoples R China
3.Res Ctr Comp Simulating & Informat Proc Biomacrom, Shenyang 110036, Liaoning, Peoples R China
4.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
5.Liaoning Univ, Sch Life Sci, Shenyang 110036, Liaoning, Peoples R China
Recommended Citation
GB/T 7714
Chen, X ,Xie, D ,Wang, L ,et al. BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction[J]. BIOINFORMATICS,2018,34(18):3178-3186.
APA Chen, X ,Xie, D ,Wang, L ,Zhao, Q ,You, ZH ,&Liu, HS .(2018).BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction.BIOINFORMATICS,34(18),3178-3186.
MLA Chen, X ,et al."BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction".BIOINFORMATICS 34.18(2018):3178-3186.
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