title: Molecular diagnosis. Classification, model selection and performance evaluation.
abstract: OBJECTIVE
  We discuss supervised classification techniques applied to medical diagnosis based on gene expression profiles. Our focus lies on strategies of adaptive model selection to avoid overfitting in high-dimensional spaces.
  
  
  METHODS
  We introduce likelihood-based methods, classification trees, support vector machines and regularized binary regression. For regularization by dimension reduction, we describe feature selection methods: feature filtering, feature shrinkage and wrapper approaches. In small sample-size situations efficient methods of data re-use are needed to assess the predictive power of a model. We discuss two issues in using cross-validation: the difference between in-loop and out-of-loop feature selection, and estimating model parameters in nested-loop cross-validation.
  
  
  RESULTS
  Gene selection does not reduce the dimensionality of the model. Tuning parameters enable adaptive model selection. The feature selection bias is a common pitfall in performance evaluation. Model selection and performance evaluation can be combined by nested-loop cross-validation.
  
  
  CONCLUSIONS
  Classification of microarrays is prone to overfitting. A rigorous and unbiased assessment of the predictive power of the model is a must.
authors.affiliation: Max Planck Institute for Molecular Genetics, Computational Diagnostics Group, Ihnestrasse 63-73, 14195 Berlin, Germany. florian.markowetz@molgen.mpg.de
authors.forename: F
authors.initials: F
authors.lastname: Markowetz
authors.forename: R
authors.initials: R
authors.lastname: Spang
mesh_terms: D020869:Gene Expression Profiling / Q000145:classification / Q000379:methods*; D036281:Genetic Research; D008432:Mathematical Computing; D015233:Models, Statistical; D025202:Molecular Diagnostic Techniques / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000145:classification / Q000379:methods*; D011336:Probability; D015203:Reproducibility of Results; D012306:Risk; D015983:Selection Bias