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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. Hoefsloot1 / Suzanne Smit2 / Age K. Smilde3

1University of Amsterdam

2University of Amsterdam

3University of Amsterdam

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 7, Issue 2, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1351, February 2008

Publication History

Published Online:
2008-02-19

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

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[1]
Alexia Kakourou, Werner Vach, and Bart Mertens
Journal of Computational Biology, 2014, Volume 21, Number 12, Page 898

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