title: Classification of genomic data: some aspects of feature selection.
abstract: Feature selection, while working with genomic data sets, is of particular interest, not only for classification (diagnostics) improvement, but also for the data interpretability. Application of the multivariate feature selection approaches allows an efficient reduction of data dimensionality, but as demonstrated in our study, sets of the selected variables depend on the objective function of the classifier. It is possible to select different subset of genes for classification due to the correlation of genes but their interpretation ought to be cautiously made.
authors.affiliation: Department of Chemometrics, Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006 Katowice, Poland.
authors.forename: Tomasz
authors.initials: T
authors.lastname: Czekaj
authors.forename: Wen
authors.initials: W
authors.lastname: Wu
authors.forename: Beata
authors.initials: B
authors.lastname: Walczak
mesh_terms: D002965:Classification; D019295:Computational Biology / Q000145:classification / Q000379:methods*; D030541:Databases, Genetic; D023281:Genomics / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical