title: One-step extrapolation of the prediction performance of a gene signature derived from a small study.
abstract: OBJECTIVE
  Microarray-related studies often involve a very large number of genes and small sample size. Cross-validating or bootstrapping is therefore imperative to obtain a fair assessment of the prediction/classification performance of a gene signature. A deficiency of these methods is the reduced training sample size because of the partition process in cross-validation and sampling with replacement in bootstrapping. To address this problem, we aim to obtain a prediction performance estimate that strikes a good balance between bias and variance and has a small root mean squared error.
  
  
  METHODS
  We propose to make a one-step extrapolation from the fitted learning curve to estimate the prediction/classification performance of the model trained by all the samples.
  
  
  RESULTS
  Simulation studies show that the method strikes a good balance between bias and variance and has a small root mean squared error. Three microarray data sets are used for demonstration.
  
  
  CONCLUSIONS
  Our method is advocated to estimate the prediction performance of a gene signature derived from a small study.
authors.affiliation: Research Center for Genes, Environment and Human Health, and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan Department of Medical Research, Tzu Chi General Hospital, Hualien, Taiwan.
authors.forename: Ling-Yi
authors.initials: LY
authors.lastname: Wang
authors.affiliation: Research Center for Genes, Environment and Human Health, and Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
authors.forename: Wen-Chung
authors.initials: WC
authors.lastname: Lee
mesh_terms: D019540:Area Under Curve; D001943:Breast Neoplasms / Q000235:genetics*; D003110:Colonic Neoplasms / Q000235:genetics*; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D059032:Learning Curve; D016014:Linear Models; D009010:Monte Carlo Method; D015203:Reproducibility of Results; D018401:Sample Size; D013223:Statistics as Topic
keywords: classification; gene signature; learning curve; microarray; prediction; receiver operating characteristic curve