A classification model for the Leiden proteomics competition.
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.
Hoefsloot
Huub C J
HC
University of Amsterdam. h.c.j.hoefsloot@uva.nl
Smit
Suzanne
S
Smilde
Age K
AK
D014408:Biomarkers, Tumor / Q000097:blood; D001798:Blood Proteins / Q000737:chemistry; D001943:Breast Neoplasms / Q000097:blood / Q000145:classification / Q000175:diagnosis; D016022:Case-Control Studies; D030562:Databases, Protein; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D006801:Humans; D015233:Models, Statistical; D009426:Netherlands; D025341:Principal Component Analysis; D040901:Proteomics / Q000706:statistics & numerical data*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000706:statistics & numerical data
D014408:Biomarkers, Tumor; D001798:Blood Proteins