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Article Dans Une Revue Redox Report Année : 2009

A machine-learning approach to the prediction of oxidative stress in chronic inflammatory disease.

Résumé

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.
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Dates et versions

hal-01048615 , version 1 (25-07-2014)

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Alban Magon de La Villehuchet, Michel Brack, Gerard Dreyfus, Yacine Oussar, Dominique Bonnefont-Rousselot, et al.. A machine-learning approach to the prediction of oxidative stress in chronic inflammatory disease.. Redox Report, 2009, 14 (1), pp.23-33. ⟨10.1179/135100009X392449⟩. ⟨hal-01048615⟩
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