title: Data-mining methods as useful tools for predicting individual drug response: application to CYP2D6 data.
abstract: OBJECTIVE
  Selecting a maximally informative subset of polymorphisms to predict a clinical outcome, such as drug response, requires appropriate search methods due to the increased dimensionality associated with looking at multiple genotypes. In this study, we investigated the ability of several pattern recognition methods to identify the most informative markers in the CYP2D6 gene for the prediction of CYP2D6 metabolizer status.
  
  
  METHODS
  Four data-mining tools were explored: decision trees, random forests, artificial neural networks, and the multifactor dimensionality reduction (MDR) method. Marker selection was performed separately in eight population samples of different ethnic origin to evaluate to what extent the most informative markers differ across ethnic groups.
  
  
  RESULTS
  Our results show that the number of polymorphisms required to predict CYP2D6 metabolic phenotype with a high accuracy can be dramatically reduced owing to the strong haplotype block structure observed at CYP2D6. MDR and neural networks provided nearly identical results and performed the best.
  
  
  CONCLUSIONS
  Data-mining methods, such as MDR and neural networks, appear as promising tools to improve the efficiency of genotyping tests in pharmacogenetics with the ultimate goal of pre-screening patients for individual therapy selection with minimum genotyping effort.
authors.affiliation: Unité de Recherche en Génétique Epidémiologique et Structure des Populations Humaines, INSERM U535, Villejuif, France. sabbagh@vjf.inserm.fr
authors.forename: Audrey
authors.initials: A
authors.lastname: Sabbagh
authors.forename: Pierre
authors.initials: P
authors.lastname: Darlu
mesh_terms: D019389:Cytochrome P-450 CYP2D6 / Q000235:genetics*; D005006:Ethnicity / Q000235:genetics; D005240:Feasibility Studies; D005787:Gene Frequency; D005819:Genetic Markers; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D016571:Neural Networks, Computer; D010597:Pharmacogenetics / Q000379:methods*; D010641:Phenotype; D011110:Polymorphism, Genetic
chemical_list: D005819:Genetic Markers; D019389:Cytochrome P-450 CYP2D6