Abstract
MiRNAs are proved to be implicated in human diseases. The disease-related miRNAs are expected to be novel bio-marks for disease therapy and drug development. This work develops a Heterogeneous Graph Convolutional Network-based deep learning model, namely HGCNMDA, to perform a MiRNA-Disease Association prediction task. We construct a three-layer heterogeneous network consisting of a miRNA, a disease, and a gene layer. Then we prepare two kinds of attributes for every node in the network and refine the nodes in the network into several node types according to their attributes. After that, a heterogeneous graph convolutional network is employed to learn feature representations for miRNAs, diseases, and genes with finer-grained node type and edge type information. Finally, the miRNA-disease associations are recovered by the inner product of the miRNA features and disease features. The experimental results on the human miRNA-disease association dataset show that the HGCNMDA achieves better performance in AUC values than other five state-of-the-art models.
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References
Huang, Z., Shi, J., Gao, Y., Cui, C., Zhang, S., Li, J., et al.: HMDD v3. 0: a database for experimentally supported human microRNA–disease associations. Nucleic Acids Res. 47(D1), D1013–D1017 (2019)
Zou, Q., Li, J., Song, L., Zeng, X., Wang, G.: Similarity computation strategies in the microRNA-disease network: a survey. 15(1), 55–64 (2016). https://doi.org/10.1093/bfgp/elv024
Zeng, X., Zhang, X., Liao, Y., Pan, L.: Prediction and validation of association between microRNAs and diseases by multipath methods. Biochim. Biophys. Acta Gen. Subj. 1860(11 Pt B), 2735–2739 (2016). https://doi.org/10.1016/j.bbagen.2016.03.016
You, Z.-H., Huang, Z.-A., Zhu, Z., Yan, G.-Y., Li, Z.-W., Wen, Z., et al.: PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLoS Computat. Biol. 13(3), e1005455 (2017). https://doi.org/10.1371/journal.pcbi.1005455
Liu, Y., Zeng, X., He, Z., Zou, Q.: Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Trans. Computat. Biol. Bioinform. 14(4), 905–915 (2017). https://doi.org/10.1109/TCBB.2016.2550432
Luo, J., Xiao, Q.: A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network. J. Biomed. Inform. 66, 194–203 (2017).https://doi.org/10.1016/j.jbi.2017.01.008
Chen, X., Yan, G.-Y.: Semi-supervised learning for potential human microRNA-disease associations inference. Sci. Rep. 4(1), 1–10 (2014)
Lan, W., Wang, J., Li, M., Liu, J., Wu, F.-X., Pan, Y.: Predicting microRNA-disease associations based on improved microRNA and disease similarities. IEEE/ACM Trans. Computat. Biol. Bioinform. 15(6), 1774–1782 (2016)
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)
Yan, C., Wang, J., Ni, P., Lan, W., Wu, F.-X., Pan, Y.: DNRLMF-MDA: predicting microRNA-disease associations based on similarities of microRNAs and diseases. IEEE/ACM Trans. Computat. Biol. Bioinform. 16(1), 233–243 (2017)
Peng, W., Du, J., Dai, W., Lan, W.: Predicting miRNA-disease association based on modularity preserving heterogeneous network embedding. Front. Cell Dev. Biol. 9, 603758Â (2021)
Li, J.-Q., Rong, Z.-H., Chen, X., Yan, G.-Y., You, Z.-H.: MCMDA: matrix completion for MiRNA-disease association prediction. Oncotarget 8(13), 21187 (2017)
Chen, X., Wang, L., Qu, J., Guan, N.-N., Li, J.-Q.: Predicting miRNA–disease association based on inductive matrix completion. Bioinformatics 34(24), 4256–4265 (2018)
Chen, X., Sun, L.-G., Zhao, Y.: NCMCMDA: miRNA–disease association prediction through neighborhood constraint matrix completion. Brief. Bioinform. 22(1), 485–496 (2021)
Li, J., Zhang, S., Liu, T., Ning, C., Zhang, Z., Zhou, W.: Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction. Bioinformatics 36(8), 2538–2546 (2020)
Jiang, Q., Hao, Y., Wang, G., Juan, L., Zhang, T., Teng, M., et al.: Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst. Biol. 4(1), 1–9 (2010)
Shi, H., Xu, J., Zhang, G., Xu, L., Li, C., Wang, L., et al.: Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Syst. Biol. 7(1), 1–12 (2013)
Peng, J., Hui, W., Li, Q., Chen, B., Hao, J., Jiang, Q., et al.: A learning-based framework for miRNA-disease association identification using neural networks. Bioinformatics 35(21), 4364–4371 (2019)
Li, Z., Jiang, K., Qin, S., Zhong, Y., Elofsson, A.: GCSENet: a GCN, CNN and SENet ensemble model for microRNA-disease association prediction. PLOS Computat. Biol. 17(6), e1009048 (2021)
Peng, W., Lan, W., Yu, Z., Wang, J., Pan, Y.: A framework for integrating multiple biological networks to predict MicroRNA-disease associations. IEEE Trans. Nanobiosci. 16(2),100–107 (2016)
Peng, W., Lan, W., Zhong, J., Wang, J., Pan, Y.: A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks. Methods 124, 69–77 (2017)
Dweep, H., Gretz, N.: miRWalk2. 0: a comprehensive atlas of microRNA-target interactions. Nat. Methods 12(8), 697 (2015)
Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., et al.: The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. gkw937 (2016)
Chen, H., Zhang, Z., Feng, D.: Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis. BMC Bioinformatics 20(1), 1–8 (2019)
Acknowledgement
This work is supported in part by the National Natural Science Foundation of China (No.61972185. No. 62072124). Natural Science Foundation of Yunnan Province of China (2019FA024), Yunnan Key Research and Development Program (2018IA054), Yunnan Ten Thousand Talents Plan young.
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Che, Z., Peng, W., Dai, W., Wei, S., Lan, W. (2021). A Heterogeneous Graph Convolutional Network-Based Deep Learning Model to Identify miRNA-Disease Association. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_12
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