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Parsimonious Selection of Useful Genes in Microarray Gene Expression Data

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Software Tools and Algorithms for Biological Systems

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 696))

Abstract

Machine learning methods have of late made significant efforts to solving multidisciplinary problems in the field of cancer classification in microarray gene expression data. These tasks are characterized by a large number of features and a few observations, making the modeling a nontrivial undertaking. In this study, we apply entropic filter methods for gene selection, in combination with several off-the-shelf classifiers. The introduction of bootstrap resampling techniques permits the achievement of more stable performance estimates. Our findings show that the proposed methodology permits a drastic reduction in dimension, offering attractive solutions in terms of both prediction accuracy and number of explanatory genes; a dimensionality reduction technique preserving discrimination capabilities is used for visualization of the selected genes.

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Notes

  1. 1.

    These figures were obtained in a standard ×86 machine at 2.666 GHz.

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Author information

Authors and Affiliations

  1. Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Omega Building, North Campus, 08034, Barcelona, Spain

    Lluís A. Belanche-Muñoz

Authors
  1. Félix F. González-Navarro
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  2. Lluís A. Belanche-Muñoz
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Editor information

Editors and Affiliations

  1. Dept. Computer Science, University of Georgia, Athens, 30602-7404, Georgia, USA

    Hamid R. Arabnia

  2. , Department of Computer Science, Lamar University, Beaumont, 77710, Texas, USA

    Quoc-Nam Tran

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González-Navarro, F.F., Belanche-Muñoz, L.A. (2011). Parsimonious Selection of Useful Genes in Microarray Gene Expression Data. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_5

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