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de la Villehuchet, A. Magon; Brack, M; Dreyfus, G; Oussar, Y; Bonnefont-Rousselot, D; Chapman, M. J; Kontush, A (2009)

A MACHINE-LEARNING APPROACH TO THE PREDICTION OF OXIDATIVE STRESS IN CHRONIC INFLAMMATORY DISEASE

Redox Rep. 14(1):23-33
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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.