Predicting microRNA-disease association based on microRNA structural and functional similarity network

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.

Graphical abstract

Keywords

miRNAs; hairpin structure / miRNA families / functional similarity / disease semantic / leave-one-out cross-validation

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-019-0170-0.

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).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Tao Ding, Jie Gao, Shanshan Zhu, Junhua Xu and Min Wu declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature