Abstract
The prediction of potential associations between disease and microRNAs is of core importance for understanding disease etiology and pathogenesis. Many researchers have proposed different computational methods to predict potential associations between microRNAs and diseases. Considering the limitations in previous methods, we developed HyperGraph for MiRNA-Disease Association (HGMDA) to uncover the relationship between diseases and microRNAs. Firstly, the miRNA functional similarity, the disease semantic similarity, and known miRNA–disease associations were used to form an informative feature vector. Then the vector for known associated pairs obtained from the HMDD v2.0 database was used to construct hypergraph. Finally, inductive hypergraph learning was used for predicting miRNA-disease associations. Experimental results show that the proposed method is effective for miRNA-disease association predication.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ambros, V.: microRNAs: tiny regulators with great potential. Cell 107, 823–826 (2001)
Xu, J., et al.: Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer. Mol. Cancer Ther. 10, 1857–1866 (2011)
Chen, X., Yan, G.Y.: Semi-supervised learning for potential human microRNA-disease associations inference. Sci. Rep. 4, 5501 (2014)
Xiao, Q., Luo, J., Liang, C., Cai, J., Ding, P.: A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations. Bioinformatics 34(2), 239–248 (2018)
Jiang, Y., Liu, B., et al.: Predict MiRNA-disease association with collaborative filtering. Neuroinformatics 16, 363–372 (2018)
Li, Y., Qiu, C., et al.: HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 42, D1070–D1074 (2014)
Zhang, Z., et al.: Inductive multi-hypergraph learning and its application on view-based 3D object classification. IEEE Trans. Image Process. 27(12), 5957–5968 (2018)
Shao, B.Y., Liu, B.T., Yan, C.G.: SACMDA: MiRNA-disease association prediction with short acyclic connections in heterogeneous graph. Neuroinformatics 16, 373–382 (2018)
Yang, Z., Ren, F., Liu, C., et al.: dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genom. 11(4 Suppl), S5 (2010)
Jiang, Q., et al.: miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 37(Database issue), D98–D104 (2009)
Acknowledgement
This work was supported by grants from the National Natural Science Foundation of China (No. 61873001).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, QW., Wang, YT., Gao, Z., Zhang, MW., Ni, JC., Zheng, CH. (2019). HGMDA: HyperGraph for Predicting MiRNA-Disease Association. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-26969-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26968-5
Online ISBN: 978-3-030-26969-2
eBook Packages: Computer ScienceComputer Science (R0)