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Interpreting Gene Profiles from Biomedical Literature Mining with Self Organizing Maps

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Abstract

We present an approach to interpret gene profiles derived from biomedical literature using Self Organizing Maps (SOMs). Comparison of different clustering algorithms shows that SOMs perform better in grouping high dimensional gene profiles when a lot of noise is present in the data. Qualitative analysis of the clustering results prove that SOMs allow an in-depth interpretation of gene profiles with biological relevance.

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© 2006 Springer-Verlag Berlin Heidelberg

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Yu, S., Van Vooren, S., Coessens, B., De Moor, B. (2006). Interpreting Gene Profiles from Biomedical Literature Mining with Self Organizing Maps. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_93

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  • DOI: https://doi.org/10.1007/11760191_93

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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