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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Acknowledgements
This research is supported by Major Research Plan of National Natural Science Foundation of China (No. 91730301), Key Projects of National Natural Science Foundation of China (No.11831015) and the State Scholarship Fund of China (No. 201806790020).
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Author summary: microRNAs (miRNAs) are functionally related to the development and progression of various human diseases. To improve the prediction accuracy of miRNA-disease association and capture more potential disease-related miRNAs, we constructed a precise miRNA global similarity network via calculating the miRNA similarity of secondary structures, families, and functions. The novel miRNA similarity network combining precise function network with global structure network of miRNAs showed the better performance compared with others similarity network and could be used to predict the associations between miRNAs and diseases in various models.
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Ding, T., Gao, J., Zhu, S. et al. Predicting microRNA-disease association based on microRNA structural and functional similarity network. Quant Biol 7, 138–146 (2019). https://doi.org/10.1007/s40484-019-0170-0
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DOI: https://doi.org/10.1007/s40484-019-0170-0