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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

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Volume 11, Issue 5

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A PAUC-based Estimation Technique for Disease Classification and Biomarker Selection

Matthias Schmid / Torsten Hothorn / Friedemann Krause / Christina Rabe
Published Online: 2012-10-01 | DOI: https://doi.org/10.1515/1544-6115.1792

Abstract

The partial area under the receiver operating characteristic curve (PAUC) is a well-established performance measure to evaluate biomarker combinations for disease classification. Because the PAUC is defined as the area under the ROC curve within a restricted interval of false positive rates, it enables practitioners to quantify sensitivity rates within pre-specified specificity ranges. This issue is of considerable importance for the development of medical screening tests. Although many authors have highlighted the importance of PAUC, there exist only few methods that use the PAUC as an objective function for finding optimal combinations of biomarkers. In this paper, we introduce a boosting method for deriving marker combinations that is explicitly based on the PAUC criterion. The proposed method can be applied in high-dimensional settings where the number of biomarkers exceeds the number of observations. Additionally, the proposed method incorporates a recently proposed variable selection technique (stability selection) that results in sparse prediction rules incorporating only those biomarkers that make relevant contributions to predicting the outcome of interest. Using both simulated data and real data, we demonstrate that our method performs well with respect to both variable selection and prediction accuracy. Specifically, if the focus is on a limited range of specificity values, the new method results in better predictions than other established techniques for disease classification.

Keywords: classification; combinations of biomarkers; gradient boosting; partial area under the ROC curve; screening tests; stability selection

About the article

Published Online: 2012-10-01


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 11, Issue 5, ISSN (Online) 1544-6115, DOI: https://doi.org/10.1515/1544-6115.1792.

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[2]
Andreas Mayr, Matthias Schmid, and Balraj Mittal
PLoS ONE, 2014, Volume 9, Number 1, Page e84483
[3]
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BMC Bioinformatics, 2016, Volume 17, Number 1
[4]
A. Mayr, H. Binder, O. Gefeller, and M. Schmid
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