Abstract
A large number of studies have shown that the formation and development of diseases are closely related to the abnormal expression of miRNAs. Studying the association between miRNAs and diseases can not only effectively prevent but also be beneficial to treat the disease. However, traditional biological experiments need a lot of manpower, material resources. Therefore, for exploring the potential associations between miRNAs and diseases, we proposed a new model called MIXHOPMDA. First, we construct heterogeneous bipartite graph of miRNA and disease, and ensure feature vectors of miRNA and disease are projected into the same dimensional vector space. Second, we use the delta operator to operate on different powers of different adjacency matrices simultaneously. Thirdly, we obtain the scoring matrix through the fully connected layer and then select the specified threshold to judge the possibility of the potential association between miRNA and disease. On HMDD v2.0, the average AUC of our model MIXHOPMDA based on 5-fold cross-validation is 93.37%. We conduct case studies on esophageal neoplasms and finally found that 47 of the top 50 miRNAs are verified by dbDEMC or miR2Disease. In conclusion, MIXHOPMDA can be used as a new model to predict the potential associations between miRNAs and diseases. The source code and related datasets can be obtained from https://github.com/SouthLion/MXHOPMDA.
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References
Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2), 281–297 (2004)
Xu, P., Guo, M., Hay, B.A.: MicroRNAs and the regulation of cell death. Trends Genet. 20(12), 617–624 (2004)
Bartel, D.P.: MicroRNAs: target recognition and regulatory functions. Cel 136(2), 215–233 (2009)
Lu, M., Zhang, Q., Deng, M., Miao, J., Guo, Y., Gao, W., Cui, Q.: An analysis of human MicroRNA and disease associations. PLoS One 3(10), e3420 (2008)
Li, M.: Role of miR-10b in breast cancer metastasis. Breast Cancer Res. 12(5), 210 (2010)
Ma, L., Teruya-Feldstein, J., Weinberg, R.A.: Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 449(7163), 682–688 (2007)
Zhen, Y., et al.: dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics 11(4), S5 (2010)
Li, Y., et al.: HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucl. Acids Res. 42(D1), D1070–D1074 (2014)
Jiang, Q., et al.: miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucl. Acids Res. 37(S1), D98–D104 (2009)
Jiang, Q., et al.: Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst. Biol. 4(S1), S2 (2010)
Xuan, P., et al.: Correction: prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS ONE 8(9), e70204 (2013)
Chen, X., Gong, Y., Zhang, D.H., You, Z.H., Li, Z.W.: DRMDA: deep representations-based miRNA-disease association prediction. J. Cell Mol. Med. 22(1), 472–485 (2018)
Li, J., Li, Z., Nie, R., You, Z., Bao, W.: FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks. Mol. Genet. Genomics 295(5), 1197–1209 (2020)
Li, Z., Li, J., Nie, R., You, Z.-H., Bao, W.: A graph auto-encoder model for miRNA-disease associations prediction. Briefings Bioinform. 22(4), bbaa240 (2021)
Wang, D., Wang, J., Lu, M., Song, F., Cui, Q.: Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 26(13), 1644–1650 (2010)
Shao, B., Liu, B., Yan, C.: SACMDA: MiRNA-disease association prediction with short acyclic connections in heterogeneous graph. Neuroinformatics 16(3–4), 373–382 (2018)
Jiang, Y., Liu, B., Yu, L., Yan, C., Bian, H.: Predict MiRNA-disease association with collaborative filtering. Neuroinformatics 16(3–4), 363–372 (2018)
Zheng, K., You, Z.-H., Wang, L., Zhou, Y., Li, L.-P., Li, Z.-W.: DBMDA: a unified embedding for sequence-based miRNA similarity measure with applications to predict and validate miRNA-disease associations. Mol. Ther.-Nucleic Acids 19, 602–611 (2020)
Wu, Q., Wang, Y., Gao, Z., Ni, J., Zheng, C.: MSCHLMDA: multi-similarity based combinative hypergraph learning for predicting MiRNA-disease association. Front. Genet. 11, 354 (2020)
Zhou, S., Wang, S., Wu, Q., Azim, R., Li, W.: Predicting potential miRNA-disease associations by combining gradient boosting decision tree with logistic regression. Comput. Biol. Chem. 85, 107200 (2020)
Yu, S.-P., Liang, C., Xiao, Q., Li, G.-H., Ding, P.-J., Luo, J.-W.: MCLPMDA: a novel method for miRNA-disease association prediction based on matrix completion and label propagation. J. Cell Mol. Med. 23(2), 1427–1438 (2019)
Funding
This study was supported by Science and technology innovation project of Shanxi province universities (2019L0683), Changzhi Medical College Startup Fund for PhD faculty (BS201922), Provincial university innovation and entrepreneurship training programs (2019403).
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Zhang, Z., Han, P., Li, Z., Nie, R., Wang, Q. (2022). Prediction of MiRNA-Disease Association Based on Higher-Order Graph Convolutional Networks. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_15
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