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Statistical Applications in Genetics and Molecular Biology

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A Classification Model for the Leiden Proteomics Competition

Huub C. J. Hoefsloot
  • University of Amsterdam
/ Suzanne Smit
  • University of Amsterdam
/ Age K. Smilde
  • University of Amsterdam
Published Online: 2008-02-19 | DOI: https://doi.org/10.2202/1544-6115.1351

A strategy is presented to build a discrimination model in proteomics studies. The model is built using cross-validation. This cross-validation step can simply be combined with a variable selection method, called rank products. The strategy is especially suitable for the low-samples-to-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, Principal Component Discriminant Analysis is used; however, the methodology can be used with any classifier. A data set containing serum samples from breast cancer patients and healthy controls is analysed. Double cross-validation shows that the sensitivity of the model is 82% and the specificity 86%. Potential putative biomarkers are identified using the variable selection method. In each cross-validation loop a classification model is built. The final classification uses a majority voting scheme from the ensemble classifier.

Keywords: classification; curse of dimensionality; statistical validation; double cross-validation; principal component discriminant analysis; biomarker discovery; rank products

About the article

Published Online: 2008-02-19

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1351. Export Citation

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