Year: 2020
Type: article
Study design: Observational Study
Source: Nature
Authors: Hannah V. Meyer, Timothy J. W. Dawes, Marta Serrani, Wenjia Bai, Paweł Tokarczuk, Jiashen Cai, Antonio de Marvao, Albert Henry, R. Thomas Lumbers, Jakob Gierten, Thomas Thumberger, Joachim Wittbrodt, James S. Ware, Daniel Rueckert, Paul M. Matthews, Sanjay K. Prasad, Maria L. Costantino, Stuart A. Cook, Ewan Birney, Declan P. O’Regan
Institutions: European Bioinformatics Institute, Cold Spring Harbor Laboratory, MRC London Institute of Medical Sciences, Imperial College London, University of Cambridge, Politecnico di Milano, SingHealth, National University of Singapore, Duke-NUS Medical School, University College London, St Bartholomew's Hospital, Health Data Research UK, Heidelberg University, University Hospital Heidelberg, Royal Brompton & Harefield NHS Foundation Trust, UK Dementia Research Institute, National Heart Centre Singapore
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: Joachim Herz Stiftung, Wellcome Trust, European Bioinformatics Institute, European Federation of Pharmaceutical Industries and Associations, UK Research and Innovation, NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, National Institute for Health and Care Research, British Heart Foundation, Academy of Medical Sciences, Imperial College London, Imperial College Healthcare NHS Trust, University of Bristol, Deutsche Herzstiftung, University College London Hospitals NHS Foundation Trust, UK Dementia Research Institute, National Institutes of Health, Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Medical Research Council, National Cancer Institute, National Institute of Environmental Health Sciences
Awards: G1452829347, Research Excellence (round 3), G1921900218, G2028656179, G2259971274, The medaka Kiyosu panel: dissecting GxE effects of environmental chemicals, G2490065153, G2562616, G4012304641, Machine learning to model disease mechanisms and predict outcomes in cardiomyopathy, G4450932479, 'Unlocking therapeutic innovation in heart failure through genomic data science, G5005581103, G564525460, NIHR Senior Investigator Award, Genomic dissection of inherited cardiomyopathies: identifying alleles, genes and mechanisms, for patient stratification., G640549421, Using machine learning to predict clinical outcomes in heart failure, G6582518128, Defining the genetics, biomarkers and outcomes for dilated cardiomyopathy: a prospective multi- centre study (GO-DCM), G666505512, G7227888905, G7525596225, G7631291304, G780909024, G8410363198, G849084591, G8984933, Genomic dissection of inherited cardiomyopathies: identifying alleles, genes and mechanisms, for patient stratification., CSHL Cancer Center Support Grant
OpenAlex ID: W3053204930