title: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method.
abstract: BACKGROUND
  We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm.
  
  
  METHODS
  Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples.
  
  
  RESULTS
  The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification.
  
  
  BACKGROUND
  The method is available at http://dir.niehs.nih.gov/microarray/datamining
  
  
  BACKGROUND
  LI3@niehs.nih.gov
authors.affiliation: Biostatistics Branch Laboratory of Structural Biology, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA. LI3@niehs.nih.gov
authors.forename: L
authors.initials: L
authors.lastname: Li
authors.forename: C R
authors.initials: CR
authors.lastname: Weinberg
authors.forename: T A
authors.initials: TA
authors.lastname: Darden
authors.forename: L G
authors.initials: LG
authors.lastname: Pedersen
mesh_terms: D000465:Algorithms; D003106:Colon / Q000473:pathology; D003110:Colonic Neoplasms / Q000235:genetics*; D016208:Databases, Factual; D015870:Gene Expression; D006801:Humans; D016393:Lymphoma, B-Cell / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity