Abstract
Learning latent feature representation embedding in high-dimensional gene expression data is a crucial step for gene clustering application. Our clustering-framework method, incorporating Variational Autoencoders(VAE) into Self-Organizing Map (SOM), not only clustered gene expression data precisely, but also reduced the dimensionality of raw data effectively without any prior knowledge. The clustering results obtained from this method based on four gene datasets exhibited an impressive performance in efficiency and accuracy.
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References
Stears, R.L., Martinsky, T., Schena, M.: Trends in microarray analysis. Nat. Med. 9(1), 140–145 (2003)
Brown, P.O., Botstein, D.: Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21, 33–37 (1999)
Robert, C.: Machine learning, a probabilistic perspective. Chance 27(2), 62–63 (2014)
Seonwoo, M., Byunghan, L., Sungroh, Y.: Deep learning in bioinformatics. Brief. Bioinform. 18, 851–869 (2017)
Lecun, Y., Bengio, Y., Hinton, G.: Deep Learn. 521(7553), 436–444 (2015)
Ravi, D., et al.: Deep learning for health informatics. IEEE J. Biomed. Health Inf. 21(1), 4–21 (2017)
Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. Inf. Fusion 42, 146–157 (2018)
Angermueller, C., Parnamaa, T., Parts, L., Stegle, O.: Deep learning for computational biology. Mol. Syst. Biol. 12(7), 878 (2016)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length and Helmholtz free energy. In: Advances in neural information processing systems 6 (1994)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Weng, R., Lu, J., Tan, Y.P., Zhou, J.: Learning cascaded deep auto-encoder networks for face alignment. IEEE Trans. Multi. 18(10), 1 (2016)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine learning, pp. 1096–1103 (2008)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: Conference Proceedings: Papers Accepted to the International Conference on Learning Representations (ICLR) Ithaca, NY: arXiv.org. (2014)
Doersch C.: Tutorial on variational autoencoders (2016)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000)
Zou, X., Sun, H.: Clustering analysis of micro-array data based on the SOM algorithm. In: Proceedings of the 2013 Ninth International Conference on Computational Intelligence and Security, pp. 308–312. IEEE (2013)
Kohonen T.: The self-organizing map. Neurocomputing 21(1/2/3), 1–6 (1990)
Kaski, S., Peltonen, J.: Dimensionality reduction for data visualization [applications corner]. IEEE Sig. Process. Mag. 28(2), 100–104 (2011)
Hu, Q., Greene, C.S.: Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics. Pac. Symp. Biocomput. 4, 362–373 (2019)
Jaskowiak, P.A., Campello, R., Costa, I.G.: Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis. IEEE/ACM Trans. Comput. Biol. Bioinf. 10(4), 845–857 (2013)
Ben-Dor, B., Chor, R., Karp, Y.Z.: Discovering local structure in gene expression data: the order-preserving submatrix problem. In: Proceedings of the Sixth Annual International Conference on Computational Biology (RECOMB 2002), pp. 49–57 (2002)
Yang, J., Wang, H., Wang, W., Yu, P.: An improved biclustering method for analyzing gene expression profiles. Int. J. Artif. Intell. Tools 14(05), 771–789 (2005)
Amela, P., Bleuler, S., Zimmermann, P., Wille, A., Zitzler, E.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)
Saber, H., Elloumi, M.: A new study on biclustering tools, biclusters validation and evaluation functions. Int. J. Comput. Sci. Eng. Surv. 6(1), 01–13 (2015)
Acknowledgements
We acknowledge the financial support from the China Postdoctoral Science Foundation (2020M671554), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0921, KYCX19_0985).
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Cui, Y., Gao, H., Zhang, R., Lu, Y., Xue, Y., Zheng, CH. (2020). A Novel Clustering-Framework of Gene Expression Data Based on the Combination Between Deep Learning and Self-organizing Map. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_1
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