A machine-learning approach to the prediction of oxidative stress in chronic inflammatory disease.
Redox Rep
; 14(1): 23-33, 2009.
Article
en En
| MEDLINE
| ID: mdl-19161675
ABSTRACT
Oxidative stress is implicated in the development of a wide range of chronic human diseases, ranging from cardiovascular to neurodegenerative and inflammatory disorders. As oxidative stress results from a complex cascade of biochemical reactions, its quantitative prediction remains incomplete. Here, we describe a machine-learning approach to the prediction of levels of oxidative stress in human subjects. From a database of biochemical analyses of oxidative stress biomarkers in blood, plasma and urine, non-linear models have been designed, with a statistical methodology that includes variable selection, model training and model selection. Our data demonstrate that, despite a large inter- and intra-individual variability, levels of biomarkers of oxidative damage in biological fluids can be predicted quantitatively from measured concentrations of a limited number of exogenous and endogenous antioxidants.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Biomarcadores
/
Estrés Oxidativo
/
Antioxidantes
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Redox Rep
Asunto de la revista:
BIOQUIMICA
/
METABOLISMO
Año:
2009
Tipo del documento:
Article
País de afiliación:
Francia