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利用深度稀疏自动编码器预测miRNA与疾病的关联关系
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Abstract:
microRNA (miRNA)是一类具有调控功能的内源性非编码RNA，在各种生物的生命发展过程中发挥着关键作用。许多生物学实验研究证明，miRNA与人类疾病密切相关，包括疾病的发生、流行、传播、诊断和治疗。但是生物实验既昂贵又耗时。因此，有效的计算模型变得越来越重要。在这项研究中，我们将稀疏性嵌入到现有的自动编码器中，形成一个新的计算框架(DSAEMDA)。首先，通过两种方式计算疾病语义相似度得到两个疾病语义相似度矩阵，计算miRNA功能相似度得到miRNA功能相似度矩阵，分别同疾病和miRNA的高斯相互作用谱核相似度矩阵融合。然后嵌入高维空间提取疾病和miRNA高维表达，利用已被证明的miRNA-疾病关联数据训练我们的深度稀疏自动编码器。此外，通过计算未知关系对的重建误差可用于预测某些疾病相关miRNA的相关值。实验结果表明，DSAEMDA可以有效地预测疾病相关的miRNA且准确率高。
MicroRNA (miRNA) is a series of endogenous non-coding RNAs with regulatory functions that take a key part in the life development of various organisms. Many biological experimental studies have proved that miRNA is closely allied to human diseases, including the occurrence, prevalence, transmission, diagnosis and treatment of diseases. But biological experiments are expensive as well as time-consuming. Therefore, efficient computational models to avoid the above problems are be-coming increasingly necessary to identify potential miRNA-disease associations. In this study, we add sparsity to the existing autoencoder to form a new computational framework named DSAEMDA (deep sparse autoencoder miRNA-disease association). First, two disease semantic similarity matrices were obtained by computing disease semantic similarity in two ways, and miRNA functional similarity matrices were obtained by computing miRNA functional similarity, which were fused with the kernel similarity matrix of Gaussian interaction spectrum of disease and miRNA respectively. Then, disease and miRNA high-dimensional expressions were extracted by embedding in high-dimensional space, and our deep sparse autoencoder was trained by using proven miR-NA-disease association data. In addition, the reconstruction errors of unknown relationship pairs can be used to predict the correlation values of some disease-related miRNAs. The experimental results showed that DSAEMDA could effectively predict disease-related miRNA with high accuracy.
| [1] | Ambros, V. (2001) microRNAs: Tiny Regulators with Great Potential. Cell, 107, 823-826.
https://doi.org/10.1016/S0092-8674(01)00616-X |
| [2] | Ambros, V. (2004) The Functions of Animal microRNAs. Nature, 431, 350-355. https://doi.org/10.1038/nature02871 |
| [3] | Bartel, D.P. (2009) MicroRNAs: Target Recogni-tion and Regulatory Functions. Cell, 136, 215-233.
https://doi.org/10.1016/j.cell.2009.01.002 |
| [4] | Kozomara, A. and Griffiths-Jones, S. (2011) miRBase: Integrating microRNA Annotation and Deep-Sequencing Data. Nucleic Acids Research, 39, D152-D157. https://doi.org/10.1093/nar/gkq1027 |
| [5] | Jopling, C.L., Yi, M.K., Lancaster, A.M., Lemon, S.M. and Sarnow, P. (2005) Modulation of Hepatitis C Virus RNA Abundance by a Liver-Specific MicroRNA. Science, 309, 1577-1581. https://doi.org/10.1126/science.1113329 |
| [6] | Lee, R.C., Feinbaum, R.L. and Ambros, V. (1993) The C. elegans Heterochronic Gene lin-4 Encodes small RNAs with Antisense Complementarity to lin-14. Cell, 89, 1828-1835. https://doi.org/10.1016/0092-8674(93)90529-Y |
| [7] | Cheng, A.M., Byrom, M.W., Shelton, J. and Ford, L.P. (2005) Antisense Inhibition of Human miRNAs and Indications for an Involvement of miRNA in Cell Growth and Apoptosis. Nucleic Acids Research, 33, 1290-1297.
https://doi.org/10.1093/nar/gki200 |
| [8] | Karp, X. and Ambros, V. (2005) Encountering microRNAs in Cell Fate Signaling. Science, 310, 1288-1289.
https://doi.org/10.1126/science.1121566 |
| [9] | Miska, E.A. (2005) How microRNAs Control Cell Division, Differ-entiation and Death. Current Opinion in Genetics & Development, 15, 563-568. https://doi.org/10.1016/j.gde.2005.08.005 |
| [10] | Xu, P.Z., Guo, M. and Hay, B.A. (2004) MicroRNAs and the Reg-ulation of Cell Death. Trends in Genetics, 20, 617-624. https://doi.org/10.1016/j.tig.2004.09.010 |
| [11] | Jiang, Q., Hao, Y. and Wang, G. (2010) Prioritization of Disease microRNAs through a Human Phenome-microRNAome Network. BMC Systems Biology, 4, S2. https://doi.org/10.1186/1752-0509-4-S1-S2 |
| [12] | Shi, H., Xu, J. and Zhang, G. (2013) Walking the Interactome to Identify Human miRNA-Disease Associations through the Functional Link between miRNA Targets and Disease Genes. BMC Systems Biology, 7, Article No. 101.
https://doi.org/10.1186/1752-0509-7-101 |
| [13] | M？rk, S., Pletscher-Frankild, S., Palleja Caro, A., Gorodkin, J. and Jensen, L.J. (2014) Protein-Driven Inference of miRNA-Disease Associations. Bioinformatics (Oxford, England), 30, 392-397.
https://doi.org/10.1093/bioinformatics/btt677 |
| [14] | Chen, X., Liu, M.X. and Yan, G.Y. (2012) RWRMDA: Pre-dicting Novel Human microRNA-Disease Associations. Molecular BioSystems, 8, 2792-2798. https://doi.org/10.1039/c2mb25180a |
| [15] | Chen, X., Yan, C.C., Zhang, X., You, Z.H., Deng, L., Liu, Y., Zhang, Y. and Dai, Q. (2016) WBSMDA: Within and between Score for MiRNA-Disease Association Prediction. Scientific Re-ports, 6, Article No. 21106.
https://doi.org/10.1038/srep21106 |
| [16] | Chen, X., Yan, C., Zhang, X., You, Z., Huang, Y. and Yan, G. (2016) HGIMDA: Heterogeneous Graph Inference for MiRNA-Disease Association Prediction. Oncotarget, 7, 65257-65269. https://doi.org/10.18632/oncotarget.11251 |
| [17] | Xu, J., Li, C.X., Lv, J.Y., Li, Y.S., Xiao, Y., Shao, T.T., Huo, X., Li, X., Zou, Y., Han, Q.L., Li, X., Wang, L.H. and Ren, H. (2011) Prioritizing Candidate Disease miRNAs by Topolog-ical Features in the miRNA Target-Dysregulated Network: Case Study of Prostate Cancer. Molecular Cancer Therapeu-tics, 10, 1857-1866.
https://doi.org/10.1158/1535-7163.MCT-11-0055 |
| [18] | Chen, X. and Yan, G.Y. (2014) Semi-Supervised Learning for Potential Human microRNA-Disease Associations Inference. Scientific Reports, 4, Article No. 5501. https://doi.org/10.1038/srep05501 |
| [19] | Chen, X. (2015) Predicting lncRNA-Disease Associations and Construct-ing lncRNA Functional Similarity Network Based on the Information of miRNA. Scientific Reports, 5, Article No. 13186. https://doi.org/10.1038/srep13186 |
| [20] | Wang, D., Wang, J., Lu, M., Song, F. and Cui, Q. (2010) Inferring the Human microRNA Functional Similarity and Functional Network Based on microRNA-Associated Diseases. Bioinfor-matics, 26, 1644-1650.
https://doi.org/10.1093/bioinformatics/btq241 |
| [21] | Peng, J., Hui, W., Li, Q., Chen, B., Hao, J., Jiang, Q., Shang, X. and Wei, Z. (2019) A Learning-Based Framework for miRNA-Disease Association Identification Using Neural Net-works. Bioinformatics (Oxford, England), 35, 4364-4371.
https://doi.org/10.1093/bioinformatics/btz254 |
| [22] | Xuan, P., Sun, H., Wang, X., Zhang, T. and Pan, S. (2019) In-ferring the Disease Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks. International Journal of Molecular Sciences, 20, 3648. https://doi.org/10.3390/ijms20153648 |
| [23] | Fu, L. and Peng, Q. (2017) A Deep Ensemble Model to Predict miRNA-Disease Association. Scientific Reports, 7, Article No. 14482. https://doi.org/10.1038/s41598-017-15235-6 |
| [24] | Mikolov, T., Sutskever, I., Chen, K., Corrado, G. and Dean, J. (2013) Distributed Representations of Words and Phrases and Their Compositionality. Proceedings of the 26th Interna-tional Conference on Neural Information Processing Systems, Volume 2, 3111-3119. |
| [25] | Bahdanau, D., Cho, K.H. and Bengio, Y. (2015) Neural Machine Translation by Jointly Learning to Align and Translate. 3rd International Con-ference on Learning Representations, ICLR 2015, San Diego, 7-9 May 2015. |
| [26] | Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K. (2019) BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. Pro-ceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Hu-man Language Technologies (Long and Short Papers), Volume 1, 4171-4186. |
| [27] | Tan, P.-N., Steinbach, M. and Ku-mar, V. (2005) Introduction to Data Mining. Addison-Wesley Longman Publishing, Boston. |
| [28] | Manning, C.D., Raghavan, P. and Schutze, H. (2008) Introduction to Information Retrieval. Cambridge University Press, Cam-bridge. |
| [29] | Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning Representations by Back-Propagating Errors. Nature, 323, 533-536. https://doi.org/10.1038/323533a0 |
| [30] | Nair, V. and Hinton, G.E. (2010) Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, 21-24 June 2010, 807-814. |
| [31] | Ng, A. (2011) Sparse Autoencoder. CS294A Lecture Notes, Vol. 72, 1-19. |
| [32] | Rifai, S., Vincent, P., Muller, X., Glorot, X. and Bengio, Y. (2011) Contractive Auto-Encoders: Explicit In-variance during Feature Extraction. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, 28 June-2 July 2011, 833-840. |
| [33] | Kingma, D.P. and Ba, J. (2014) Adam: A Method for Stochastic Optimi-zation. |
| [34] | Ji, C., Gao, Z., Ma, X., Wu, Q. and Zheng, C. (2021) AEMDA: Inferring miRNA-Disease Associations Based on Deep Autoencoder. Bioinformatics, 37, 66-72. https://doi.org/10.1093/bioinformatics/btaa670 |
| [35] | Gao, Z., Wang, Y.-T., Wu, Q.-W., Ni, J.-C. and Zheng, C.-H. (2020) Graph Regularized L2,1-Nonnegative Matrix Factoriza-tion for miRNA-Disease Association Prediction. BMC Bioinformatics, 21, Article No. 61.
https://doi.org/10.1186/s12859-020-3409-x |
| [36] | Jiang, Y., Liu, B., Yu, L., Yan, C. and Bian, H. (2018) Predict MiRNA-Disease Association with Collaborative Filtering. Neuroinformatics, 16, 363-372. https://doi.org/10.1007/s12021-018-9386-9 |
| [37] | Shao, B., Liu, B. and Yan, C. (2018) SACMDA: MiR-NA-Disease Association Prediction with Short Acyclic Connections in Heterogeneous Graph. Neuroinformatics, 16, 373-382. https://doi.org/10.1007/s12021-018-9373-1 |
| [38] | Chen, X., Wang, L., Qu, J., Guan, N.N. and Li, J.Q. (2018) Predicting miRNA-Disease Association Based on Inductive Matrix Completion. Bioinformatics, 34, 4256-4265. https://doi.org/10.1093/bioinformatics/bty503 |
