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.

DOI: 10.1089/cmb.2014.0125

Cite this paper

@article{Kakourou2014CombinationAI, title={Combination Approaches Improve Predictive Performance of Diagnostic Rules for Mass-Spectrometry Proteomic Data}, author={Alexia Kakourou and Werner Vach and Bart J. A. Mertens}, journal={Journal of computational biology : a journal of computational molecular cell biology}, year={2014}, volume={21 12}, pages={898-914} }