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 timeconsuming
Objective: Thus, computational methods are urgently needed to effectively predict miRNA-disease
Methods: In this paper, we proposed a novel method, BIRWMDA, based on a bi-random walk
model to predict miRNA-disease 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 miRNA-disease 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 literature, 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: The fusion of multiple biological networks could effectively predict miRNA-diseases
associations. We expected BIRWMDA to serve as a biological tool for mining potential miRNAdisease