Abstract
Next-generation sequencing techniques produce an enormous amount of sequence data. Analyzing these sequences requires an efficient method that can handle large amounts of data. Self-organizing maps (SOMs), which use the frequencies of N-tuples, can categorize sets of DNA sequences with unsupervised learning. In this study, SOM using correlation coefficients among nucleotides was proposed, and its performance was examined in the experiments through mapping experiments of the genome sequences of several species and classification experiments using Pareto learning SOMs.
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References
illuminal, An Introduction to Next-Generation Sequencing Technology, http://www.illumina.com/Documents/products/Illumina_Sequencing_Introduction.pdf
Kohonen, T.: Self Organizing Maps. Springer (2001)
Abe, T., Ikekura, T., et al.: Informatics for unreveiling hidden genome signatures. Genome Res. 13, 693–702
Dozono, H., Kabashima, T., et al.: Visualization of the Packet Flows using Self Organizing Maps. WSEAS Transactions on Information Science & Applications 7(1), 132–141 (2010)
Sorjamaa, A., Corona, F., Miche, Y., Merlin, P., Maillet, B., Séverin, E., Lendasse, A.: Sparse Linear Combination of SOMs for Data Imputation: Application to Financial Database. In: PrÃncipe, J.C., Miikkulainen, R. (eds.) WSOM 2009. LNCS, vol. 5629, pp. 290–297. Springer, Heidelberg (2009)
Dozono, H., Nakakuni, M.: Application of Supervised Pareto Learning Self Organizing Maps to Multi-modal Biometric Authentication. IPSJ Journal 49(9), 3028–3037 (2008) (in Japanese)
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© 2014 Springer International Publishing Switzerland
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Dozono, H. (2014). Visualization and Classification of DNA Sequences Using Pareto Learning Self Organizing Maps Based on Frequency and Correlation Coefficient. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_8
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DOI: https://doi.org/10.1007/978-3-319-07695-9_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07694-2
Online ISBN: 978-3-319-07695-9
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