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Parsimonious selection of useful genes in microarray gene expression data
González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing
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.
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
info:eu-repo/semantics/submittedVersion
info:eu-repo/semantics/bookPart
Springer
         

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