Título:
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Parsimonious selection of useful genes in microarray gene expression data
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Autor/a:
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González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
Abstract:
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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 non-trivial undertaking. In this work 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 both in terms of prediction accuracy and number of explanatory genes; a dimensionality reduction technique preserving discrimination capabilities is used for visualization of the selected genes. |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica -Computational biology -Data mining -Cancer -- Research -Biological data mining and knowledge discovery -Gene expression analysis -Tools and methods for computational biology and bioinformatics -Cancer informatics -Biologia computacional -Mineria de dades -Càncer -- Investigació |
Derechos:
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Tipo de documento:
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Artículo - Versión presentada Capítulo o parte de libro |
Editor:
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Springer
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