Abstract
The prediction of association between disease and microRNAs is playing an increasing important role for understanding disease etiology and pathogenesis. So far, many various computational methods have been proposed by researchers to predict the potential associations between microRNAs and diseases. Considering that the past methods have many limitations, we developed Random Walking with Restart based Network Consistency Projection for Predicting miRNA-disease Association to uncover the relationship between diseases and miRNAs. Based on diverse similarity measures, the proposed model constructed the topological similarity of miRNAs and diseases by random walking with restart (RWR) algorithm on the similarity network, which took full advantage of the network topology information, and introduced the Gaussian interaction profile kernel similarity to get the integrated similarity of miRNA and disease, respectively. Then, we projected miRNA space and disease space on the miRNA-disease interaction network, respectively. Finally, we can obtain the predicted miRNA-disease association score matrix by combining the above two space projection scores. Simulation results showed that RWRNCP can efficiently infer miRNA-disease relationships with high accuracy, obtaining AUCs of 0.9479 and 0.9274 in leave-one-out cross validation (LOOCV) and five-fold cross validation (5CV), respectively. Furthermore, a case study also suggested that RWRNCP is promising for discover new miRNA-disease interactions.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Nos. 61873001, U19A2064, 61872220, 11701318), Natural Science Foundation of Shandong Province (grant number ZR2020KC022), and the Open Project of Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, No. MMC202006.
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Zhang, MW., Wang, YT., Gao, Z., Li, L., Ni, JC., Zheng, CH. (2021). RWRNCP: Random Walking with Restart Based Network Consistency Projection for Predicting miRNA-Disease Association. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_47
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DOI: https://doi.org/10.1007/978-3-030-84532-2_47
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