Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.
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| Title: | Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits. |
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| Authors: | Glastonbury CA; Big Data Institute, University of Oxford, Oxford, United Kingdom.; BenevolentAI, London, United Kingdom., Pulit SL; Big Data Institute, University of Oxford, Oxford, United Kingdom., Honecker J; Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany., Censin JC; Big Data Institute, University of Oxford, Oxford, United Kingdom.; Wellcome Centre for Human Genetics (WCHG), Oxford, United Kingdom., Laber S; Big Data Institute, University of Oxford, Oxford, United Kingdom.; Broad Institute of MIT and Harvard, Cambridge Massachusetts, United States of America., Yaghootkar H; Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom.; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom., Rahmioglu N; Wellcome Centre for Human Genetics (WCHG), Oxford, United Kingdom.; Endometriosis CaRe Centre Oxford, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom., Pastel E; Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom., Kos K; Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom., Pitt A; NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter and Royal Devon and Exeter NHS Foundation Trust Exeter, United Kingdom., Hudson M; NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter and Royal Devon and Exeter NHS Foundation Trust Exeter, United Kingdom., Nellåker C; Big Data Institute, University of Oxford, Oxford, United Kingdom.; Endometriosis CaRe Centre Oxford, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom., Beer NL; Novo Nordisk Research Centre Oxford (NNRCO), Oxford, United Kingdom., Hauner H; Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany.; Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich.; German Center of Diabetes Research, Helmholtz Center Munich, Neuherberg, Germany., Becker CM; Endometriosis CaRe Centre Oxford, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom., Zondervan KT; Wellcome Centre for Human Genetics (WCHG), Oxford, United Kingdom.; Endometriosis CaRe Centre Oxford, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom., Frayling TM; Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, United Kingdom.; NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter and Royal Devon and Exeter NHS Foundation Trust Exeter, United Kingdom., Claussnitzer M; Broad Institute of MIT and Harvard, Cambridge Massachusetts, United States of America.; University of Hohenheim, Stuttgart, Germany.; Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America., Lindgren CM; Big Data Institute, University of Oxford, Oxford, United Kingdom.; Wellcome Centre for Human Genetics (WCHG), Oxford, United Kingdom.; Broad Institute of MIT and Harvard, Cambridge Massachusetts, United States of America. |
| Source: | PLoS computational biology [PLoS Comput Biol] 2020 Aug 14; Vol. 16 (8), pp. e1008044. Date of Electronic Publication: 2020 Aug 14 (Print Publication: 2020). |
| Publication Type: | Journal Article; Meta-Analysis; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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