Variable selection using iterative reformulation of training set models for discrimination of samples: Application to gas chromatography/mass spectrometry of mouse urinary metabolites

Analytical Chemistry

Volumn 81, Issue 13, 2009, Pages 5204-5217

  Wongravee, Kanet ^a   Heinrich, Nina ^b   Holmboe, Maria ^b   Schaefer, Michele L ^c   Reed, Randall R ^c   Trevejo, Jose ^b   Brereton, Richard G ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b CHARLES STARK DRAPER LABORATORY   (United States)

^c JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS MODELS; DATA SETS; GAS CHROMATOGRAPHY/MASS SPECTROMETRY; ITERATIVE PROCEDURES; METABOLOMIC; METABOLOMIC STUDY; MODEL OPTIMIZATION; NUMBER OF COMPONENTS; OPTIMAL VARIABLES; OVERFITTING; PARTIAL LEAST SQUARES-DISCRIMINANT ANALYSIS; PLS REGRESSION; TEST SETS; TRAINING SETS; URINARY METABOLITES; VALIDATED METHODS; VARIABLE SELECTION;

CHROMATOGRAPHIC ANALYSIS; CURVE FITTING; DISCRIMINANT ANALYSIS; GAS CHROMATOGRAPHY; OPTIMIZATION;

TESTING;

ANIMAL EXPERIMENT; ARTICLE; BOOTSTRAPPING; CONTROLLED STUDY; COST BENEFIT ANALYSIS; DISCRIMINANT ANALYSIS; GAS CHROMATOGRAPHY; MASS SPECTROMETRY; MATHEMATICAL MODEL; METABOLITE; METABOLOMICS; MOUSE; NONHUMAN; PARTIAL LEAST SQUARES REGRESSION; URINALYSIS; VALIDITY;

ANIMALS; AREA UNDER CURVE; DISCRIMINANT ANALYSIS; GAS CHROMATOGRAPHY-MASS SPECTROMETRY; LEAST-SQUARES ANALYSIS; METABOLOME; METABOLOMICS; MICE; MODELS, STATISTICAL; MODELS, THEORETICAL; URINALYSIS;

EID: 67649948769     PISSN: 00032700     EISSN: None     Source Type: Journal    
DOI: 10.1021/ac900251c     Document Type: Article

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