title: Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions.
abstract: BACKGROUND
  Two practical realities constrain the analysis of microarray data, mass spectra from proteomics, and biomedical infrared or magnetic resonance spectra. One is the 'curse of dimensionality': the number of features characterizing these data is in the thousands or tens of thousands. The other is the 'curse of dataset sparsity': the number of samples is limited. The consequences of these two curses are far-reaching when such data are used to classify the presence or absence of disease.
  
  
  RESULTS
  Using very simple classifiers, we show for several publicly available microarray and proteomics datasets how these curses influence classification outcomes. In particular, even if the sample per feature ratio is increased to the recommended 5-10 by feature extraction/reduction methods, dataset sparsity can render any classification result statistically suspect. In addition, several 'optimal' feature sets are typically identifiable for sparse datasets, all producing perfect classification results, both for the training and independent validation sets. This non-uniqueness leads to interpretational difficulties and casts doubt on the biological relevance of any of these 'optimal' feature sets. We suggest an approach to assess the relative quality of apparently equally good classifiers.
authors.affiliation: Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada R3B 1Y6. Ray.Somorjai@nrc-cnrc.gc.ca
authors.forename: R L
authors.initials: RL
authors.lastname: Somorjai
authors.forename: B
authors.initials: B
authors.lastname: Dolenko
authors.forename: R
authors.initials: R
authors.lastname: Baumgartner
mesh_terms: D000465:Algorithms; D016477:Artifacts; D016000:Cluster Analysis; D004247:DNA / Q000145:classification*; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D009369:Neoplasms / Q000145:classification / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods
chemical_list: D004247:DNA