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Using Supervised Complexity Measures in the Analysis of Cancer Gene Expression Data Sets

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Advances in Bioinformatics and Computational Biology (BSB 2009)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5676))

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Abstract

Supervised Machine Learning methods have been successfully applied for performing gene expression based cancer diagnosis. Characteristics intrinsic to cancer gene expression data sets, such as high dimensionality, low number of samples and presence of noise makes the classification task very difficult. Furthermore, limitations in the classifier performance may often be attributed to characteristics intrinsic to a particular data set.

This paper presents an analysis of gene expression data sets for cancer diagnosis using classification complexity measures. Such measures consider data geometry, distribution and linear separability as indications of complexity of the classification task. The results obtained indicate that the cancer data sets investigated are formed by mostly linearly separable non-overlapping classes, supporting the good predictive performance of robust linear classifiers, such as SVMs, on the given data sets. Furthermore, we found two complexity indices, which were good indicators for the difficulty of gene expression based cancer diagnosis.

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Author information

Authors and Affiliations

  1. Center of Informatics, Federal University of Pernambuco, Recife, Brazil

    Ivan G. Costa

  2. Center of Mathematics, Computation and Cognition, ABC Fed. Univ., SP, Brazil

    Ana C. Lorena & Liciana R. M. P. y Peres

  3. Dept. of Informatics and Applied Mathematics, Fed. Univ. of Rio Grande do Norte, Brazil

    Marcilio C. P. de Souto

Authors
  1. Ivan G. Costa
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  2. Ana C. Lorena
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  3. Liciana R. M. P. y Peres
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  4. Marcilio C. P. de Souto
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Editor information

Editors and Affiliations

  1. Center of Informatics, Av. Prof. Luiz Freire, Federal University of Pernambuco, s/n, Cidade Universitária, PE 50740-540, Recife, Brazil

    Katia S. Guimarães

  2. National Library of Medicine, National Institutes of Health, National Center for Biotechnology Information, 8600 Rockville Pike, Building 38A 8S814, Bethesda, MD 20894, USA

    Anna Panchenko

  3. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Building 38A 8S814, MD 20894,, Bethesda, USA

    Teresa M. Przytycka

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Costa, I.G., Lorena, A.C., Peres, L.R.M.P.y., de Souto, M.C.P. (2009). Using Supervised Complexity Measures in the Analysis of Cancer Gene Expression Data Sets. In: Guimarães, K.S., Panchenko, A., Przytycka, T.M. (eds) Advances in Bioinformatics and Computational Biology. BSB 2009. Lecture Notes in Computer Science(), vol 5676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03223-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-03223-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03222-6

  • Online ISBN: 978-3-642-03223-3

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