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Quantitative Evaluation of Established Clustering Methods for Gene Expression Data

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Book cover Biological and Medical Data Analysis (ISBMDA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3337))

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Abstract

Analysis of gene expression data generated by microarray techniques often includes clustering. Although more reliable methods are available, hierarchical algorithms are still frequently employed. We clustered several data sets and quantitatively compared the performance of an agglomerative hierarchical approach using the average-linkage method with two partitioning procedures, k-means and fuzzy c-means. Investigation of the results revealed the superiority of the partitioning algorithms: the compactness of the clusters was markedly increased and the arrangement of the profiles into clusters more closely resembled biological categories. Therefore, we encourage analysts to critically scrutinize the results obtained by clustering.

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Author information

Authors and Affiliations

  1. Jena Centre for Bioinformatics, Hans Knöll Institute for Natural Products Research, Junior Research Group “Pattern Recognition”, Beutenbergstr. 11a, 07745, Jena, Germany

    Dörte Radke & Ulrich Möller

Authors
  1. Dörte Radke
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  2. Ulrich Möller
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Editor information

Editors and Affiliations

  1. Biomedical Informatics Group, Artificial Intelligence Lab, Polytechnical University of Madrid, Spain

    José María Barreiro

  2. Medical Bioinformatics Department, Institute of Health ‘Carlos III’, Ctra. Majadahonda-Pozuelo, km 2. 28220 Majadahonda, Madrid,  

    Fernando Martín-Sánchez

  3. Biomedical Informatics Group, Dep. Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Spain

    Víctor Maojo

  4. GRIB, IMIM/UPF Barcelona, Spain

    Ferran Sanz

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© 2004 Springer-Verlag Berlin Heidelberg

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Radke, D., Möller, U. (2004). Quantitative Evaluation of Established Clustering Methods for Gene Expression Data. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds) Biological and Medical Data Analysis. ISBMDA 2004. Lecture Notes in Computer Science, vol 3337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30547-7_40

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  • DOI: https://doi.org/10.1007/978-3-540-30547-7_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23964-2

  • Online ISBN: 978-3-540-30547-7

  • eBook Packages: Springer Book Archive

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