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A Classification Model for the Leiden Proteomics Competition

Author

Listed:
  • Hoefsloot Huub C. J.

    (University of Amsterdam)

  • Smit Suzanne

    (University of Amsterdam)

  • Smilde Age K.

    (University of Amsterdam)

Abstract

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.

Suggested Citation

  • Hoefsloot Huub C. J. & Smit Suzanne & Smilde Age K., 2008. "A Classification Model for the Leiden Proteomics Competition," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-11, February.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:2:n:8
    DOI: 10.2202/1544-6115.1351
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    Citations

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    Cited by:

    1. Gutiérrez, Luis & Gutiérrez-Peña, Eduardo & Mena, Ramsés H., 2014. "Bayesian nonparametric classification for spectroscopy data," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 56-68.
    2. Hand David J, 2008. "Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-23, December.

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