In a study involving more than 100,000 individuals in the UKBiobank, a NeuralNetwork model trained on Metabolomic data can predict disease risk for over 20 conditions and adds predictive information over clinical variables for eight common diseases.
) via application no. 51157. This project was funded by Charité – Universitätsmedizin Berlin and Einstein Foundation Berlin. The study was supported by the BMBF-funded Medical Informatics Initiative . Resources of Flaticon.com were used in the design of the figures. M. Kivimaki received relevant founding from The Wellcome Trust , the Medical Research Council and the US National Institute on Aging . E.B.v.d.A. received relevant funds from the Dutch Research Council .
Thore Buergel, Greg Ruyoga, Julius Upmeier zu Belzen, Lukas Loock, Paul Kittner, Lara Christmann, Noah Hollmann, Henrik Strangalies, Jana M. Braunger, Benjamin Wild & Roland Eils Department of Cardiology, Campus Benjamin Franklin, Charité – Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, GermanyComputational Medicine, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, GermanyMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UKDaniele Bizzarri, Erik B. van den Akker & Eline SlagboomDaniele Bizzarri & Erik B.
Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the NetherlandsScott T. Chiesa & John Deanfield Department of Endocrinology & Metabolism, Charité – Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, GermanyCenter for Cardiovascular Research, Charité – Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, GermanyDepartment of Neurology, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charité-Universitätsmedizin Berlin, Berlin, GermanyFabian KlostermannDepartment of Internal Medicine, Division of Gerontology...
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