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Clustering Analysis for Bacillus Genus Using Fourier Transform and Self-Organizing Map

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

Abstract

Because the lengths of nucleotide sequences for microorganisms are various, it is difficult to directly compare the complete nucleotide sequences among microorganisms. In this study, we adopted a method that can convert DNA sequences of microorganisms into numerical form then applied Fourier transform to the numerical DNA sequences in order to investigate the distributions of nucleotides. Also, a visualization scheme for transformed DNA sequences was proposed to help visually categorize microorganisms. Furthermore, the well-known neural network technique Self-Organizing Map (SOM) was applied to the transformed DNA sequences to draw conclusions of taxonomic relationships among the bacteria of Bacillus genus. The results show that the relationships among the bacteria are corresponding to recent biological findings.

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© 2006 Springer-Verlag Berlin Heidelberg

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Jeng, CC., Yang, IC., Hsieh, KL., Lin, CN. (2006). Clustering Analysis for Bacillus Genus Using Fourier Transform and Self-Organizing Map. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_6

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  • DOI: https://doi.org/10.1007/11893295_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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