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Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations


Qingqi Zhu, Yongxian Fan* and Xiaoyong Pan   Pages 1 - 13 ( 13 )


Background: MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are time-consuming and costly.

Objective: Thus, computational methods are urgently needed to effectively predict miRNA-disease associations. Method: In this paper, we proposed a novel method, BIRWMDA based on a bi-random walk model to predict miRNAdisease associations. Specifically, in BIRWMDA, the similarity network fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA similarity matrix and a disease-disease similarity matrix, then the miRNAdisease associations were predicted by the bi-random walk model.

Results: To evaluate the performance of BIRWMDA, we ran the leave-one-out cross validation and 5-fold cross validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067, respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were confirmed by literatures, respectively. From the perspective of exploring miRNA-related diseases, we conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted diseases were confirmed by literatures, respectively.

Conclusion: Fusion of multiple biological networks could effectively predict miRNA-diseases associations. We expected BIRWMDA to severe as a biological tool for mining potential miRNA-disease associations.


MiRNA, disease, miRNA-disease associations, similarity network fusion, bi-random walk.


School of computer and information security, Guilin University of Electronic Technology, Guilin, School of computer and information security, Guilin University of Electronic Technology, Guilin, Institute of Image Processing and Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai

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