title: Parsimonious selection of useful genes in microarray gene expression data.
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.
authors: Departament de Llenguatges i Sistemes Informatics, Universitat Politecnica de Catalunya, Omega Building, North Campus, Barcelona, Spain. belanche@lsi.upc.edu
authors: Félix F
authors: FF
authors: González-Navarro
authors: Lluís A
authors: LA
authors: Belanche-Muñoz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D057225:Data Mining; D030541:Databases, Genetic; D003937:Diagnosis, Differential; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*