Predicting microRNA-disease association based on microRNA structural and functional similarity network |
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| Authors: | Tao Ding Jie Gao Shanshan Zhu Junhua Xu Min Wu |
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| Affiliation: | 1. School of Science, Jiangnan University, Wuxi 214122, China2. School of Mathematics Statistics and Physics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK |
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| Abstract: | Background: Increasing evidences indicate that microRNAs (miRNAs) are functionally related to the development and progression of various human diseases. Inferring disease-related miRNAs can be helpful in promoting disease biomarker detection for the treatment, diagnosis, and prevention of complex diseases. Methods: To improve the prediction accuracy of miRNA-disease association and capture more potential disease-related miRNAs, we constructed a precise miRNA global similarity network (MSFSN) via calculating the miRNA similarity based on secondary structures, families, and functions. Results: We tested the network on the classical algorithms: WBSMDA and RWRMDA through the method of leave-one-out cross-validation. Eventually, AUCs of 0.8212 and 0.9657 are obtained, respectively. Also, the proposed MSFSN is applied to three cancers for breast neoplasms, hepatocellular carcinoma, and prostate neoplasms. Consequently, 82%, 76%, and 82% of the top 50 potential miRNAs for these diseases are respectively validated by the miRNA-disease associations database miR2Disease and oncomiRDB. Conclusion: Therefore, MSFSN provides a novel miRNA similarity network combining precise function network with global structure network of miRNAs to predict the associations between miRNAs and diseases in various models. |
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| Keywords: | miRNAs hairpin structure,miRNA families,functional similarity,disease semantic,leave-one-out cross-validation, |
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