A machine-learning approach to the prediction of oxidative stress in chronic inflammatory disease
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,...
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| Veröffentlicht in: | Redox report : communications in free radical research 2009, Vol.14 (1), p.23-33 |
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| container_title | Redox report : communications in free radical research |
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| creator | de la Villehuchet, A. Magon Brack, M. Dreyfus, G. Oussar, Y. Bonnefont-Rousselot, D. Chapman, M.J. Kontush, A. |
| description | 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. |
| doi_str_mv | 10.1179/135100009X392449 |
| format | Article |
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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.</description><subject>ANTIOXIDANTS</subject><subject>Antioxidants - analysis</subject><subject>Artificial Intelligence</subject><subject>BIOLOGICAL MARKERS</subject><subject>Biomarkers - blood</subject><subject>Cardiovascular Diseases - blood</subject><subject>Cardiovascular Diseases - pathology</subject><subject>Chemical Sciences</subject><subject>Chronic Disease</subject><subject>Computer Science</subject><subject>Female</subject><subject>Humans</subject><subject>Inflammation - blood</subject><subject>Inflammation - pathology</subject><subject>MACHINE LEARNING</subject><subject>Male</subject><subject>MODEL SELECTION</subject><subject>Models, Biological</subject><subject>NEURAL NETWORKS</subject><subject>Neurodegenerative Diseases - blood</subject><subject>Neurodegenerative Diseases - pathology</subject><subject>Other</subject><subject>OXIDATIVE STRESS</subject><subject>TRAINING</subject><subject>VARIABLE SELECTION</subject><issn>1351-0002</issn><issn>1743-2928</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kc9rVDEQx4Motra99yQ5CR6e5vdL8LSUagsLXhS8hSRvXjfy3ss2ybbuf2_KLi0IzmW-zHzmCzOD0CUlnyjtzWfKJSUtzC9umBDmFTqlveAdM0y_brq1u9ZmJ-hdKb-b4srot-iEGqqo6uUpcis8u7CJC3QTuLzE5Q677TanVsQ14boBvM0wxFBjWnAacfoTB1fjA-BSM5SC44LDJqclhibHyc2zqynv8RALuALn6M3opgIXx3yGfn69_nF1062_f7u9Wq27IJisnSCSDyxoKaQW2mvTj4R5raUSnsAAQfVU9CB67b0XmhAlRiIlE8Rz7yXnZ-jjwXfjJrvNcXZ5b5OL9ma1tk81QonQisoH2tgPB7Yter-DUu0cS4BpcgukXbFKaWEEYQ0kBzDkVEqG8dmZEvv0AvvvC9rI-6P3zs8wvAwcb96ALwegXSvl2T2mPA22uv2U8pjdEmKx_L_2fwGBjZLn</recordid><startdate>2009</startdate><enddate>2009</enddate><creator>de la Villehuchet, A. 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Magon</creatorcontrib><creatorcontrib>Brack, M.</creatorcontrib><creatorcontrib>Dreyfus, G.</creatorcontrib><creatorcontrib>Oussar, Y.</creatorcontrib><creatorcontrib>Bonnefont-Rousselot, D.</creatorcontrib><creatorcontrib>Chapman, M.J.</creatorcontrib><creatorcontrib>Kontush, A.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Redox report : communications in free radical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>de la Villehuchet, A. 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| ispartof | Redox report : communications in free radical research, 2009, Vol.14 (1), p.23-33 |
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| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | ANTIOXIDANTS Antioxidants - analysis Artificial Intelligence BIOLOGICAL MARKERS Biomarkers - blood Cardiovascular Diseases - blood Cardiovascular Diseases - pathology Chemical Sciences Chronic Disease Computer Science Female Humans Inflammation - blood Inflammation - pathology MACHINE LEARNING Male MODEL SELECTION Models, Biological NEURAL NETWORKS Neurodegenerative Diseases - blood Neurodegenerative Diseases - pathology Other OXIDATIVE STRESS TRAINING VARIABLE SELECTION |
| title | A machine-learning approach to the prediction of oxidative stress in chronic inflammatory disease |
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