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.
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Acknowledgement
This work was supported by grants from the National Natural Science Foundation of China (No. 61873001).
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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
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DOI: https://doi.org/10.1007/978-3-030-26969-2_25
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