title: Combination approaches improve predictive performance of diagnostic rules for mass-spectrometry proteomic data.
abstract: We consider a proteomic mass spectrometry case-control study for the construction of a diagnostic rule for patients' disease status allocation. We propose an approach for combining a collection of classifiers for the construction of a "combined" classification rule in order to enhance calibration and prediction ability. In a first stage this is achieved by building individual classifiers separately, each one using the entire proteomic data set. A double leave-one-out cross-validatory approach is used to estimate the class-predicted probabilities on which the combination method will be calibrated. The performance of the combination approach is examined both through a breast cancer proteomic data set and through simulation studies. Our experimental results indicate that in many circumstances gains in classification performance and predictive accuracy can be achieved.
authors.affiliation: 1 Department of Medical Statistics and Bioinformatics, Leiden University Medical Center , Leiden, The Netherlands .
authors.forename: Alexia
authors.initials: A
authors.lastname: Kakourou
authors.forename: Werner
authors.initials: W
authors.lastname: Vach
authors.forename: Bart
authors.initials: B
authors.lastname: Mertens
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D006801:Humans; D013058:Mass Spectrometry / Q000706:statistics & numerical data*; D015233:Models, Statistical; D040901:Proteomics / Q000706:statistics & numerical data*
keywords: classification; classifier combination; clinical mass-spectrometry-based proteomics; double cross validation