Home >> Redox Report, Volume 14, Number 1

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

Authors: de la Villehuchet, A. Magon1; Brack, M.2; Dreyfus, G.1; Oussar, Y.1; Bonnefont-Rousselot, D.3; Chapman, M.J.4; Kontush, A.4

Source: Redox Report, Volume 14, Number 1, February 2009 , pp. 23-33(11)

Publisher: Maney Publishing

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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.

Keywords: MACHINE LEARNING; OXIDATIVE STRESS; TRAINING; MODEL SELECTION; NEURAL NETWORKS; ANTIOXIDANTS; BIOLOGICAL MARKERS; VARIABLE SELECTION

Document Type: Research Article

DOI: http://dx.doi.org/10.1179/135100009X392449

Affiliations: 1: École Supérieure de Physique et de Chimie Industrielles, ESPCI–Paristech, Laboratoire d'Électronique (CNRS UMR 7084), Paris, France 2: UMR S551 'Dyslipoproteinemia and Atherosclerosis', Université Pierre et Marie Curie (Paris 6), Paris, France; INSERM, UMR S551, Paris, France 3: Department of Metabolic Biochemistry, La Pitié Hospital (AP-HP), Paris, France; Department of Biochemistry, EA 3617, Faculty of Pharmacy, Paris Descartes University, Paris, France 4: UMR S551 'Dyslipoproteinemia and Atherosclerosis', Université Pierre et Marie Curie (Paris 6), Paris, France; INSERM, UMR S551, Paris, France; AP-HP, Groupe hospitalier Pitié–Salpétrière, Paris, France

Publication date: 2009-02-01

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