Performance evaluation of algorithms for the classification of metabolic 1H NMR fingerprints.
J Proteome Res
; 11(12): 6242-51, 2012 Dec 07.
Article
em En
| MEDLINE
| ID: mdl-23116257
ABSTRACT
Nontargeted metabolite fingerprinting is increasingly applied to biomedical classification. The choice of classification algorithm may have a considerable impact on outcome. In this study, employing nested cross-validation for assessing predictive performance, six binary classification algorithms in combination with different strategies for data-driven feature selection were systematically compared on five data sets of urine, serum, plasma, and milk one-dimensional fingerprints obtained by proton nuclear magnetic resonance (NMR) spectroscopy. Support Vector Machines and Random Forests combined with t-score-based feature filtering performed well on most data sets, whereas the performance of the other tested methods varied between data sets.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Software
/
Ressonância Magnética Nuclear Biomolecular
/
Metabolômica
Tipo de estudo:
Evaluation_studies
/
Prognostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article