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Estimating classification probabilities in high-dimensional diagnostic studies

Appel, Inka ; Gronwald, Wolfram ; Spang, Rainer



Abstract

MOTIVATION: Classification algorithms for high-dimensional biological data like gene expression profiles or metabolomic fingerprints are typically evaluated by the number of misclassifications across a test dataset. However, to judge the classification of a single case in the context of clinical diagnosis, we need to assess the uncertainties associated with that individual case rather than the ...

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