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Title:
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Parsimonious selection of useful genes in microarray gene expression data
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Author:
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González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
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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. |
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Subject(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 |
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Rights:
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Document type:
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Article - Submitted version
Book Part |
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Published by:
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Springer
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