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Estimating classification probabilities in high-dimensional diagnostic studies

Appel, Inka, Gronwald, Wolfram and Spang, Rainer (2011) Estimating classification probabilities in high-dimensional diagnostic studies. Bioinformatics 27 (18), pp. 2563-2570.

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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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Item type:Article
Date:September 2011
Institutions:Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner)
Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Identification Number:
ValueType
21784795PubMed ID
10.1093/bioinformatics/btr434DOI
Classification:
NotationType
AlgorithmsMESH
Bayes TheoremMESH
HumansMESH
Kidney Diseases/urineMESH
Metabolome/geneticsMESH
Reproducibility of ResultsMESH
Urine/chemistryMESH
Dewey Decimal Classification:500 Science > 500 Natural sciences & mathematics
500 Science > 570 Life sciences
600 Technology > 610 Medical sciences Medicine
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Item ID:30625
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