Interpretable machine learning leverages proteomics to improve cardiovascular disease risk prediction and biomarker identification.

Journal: Communications medicine
Published Date:

Abstract

BACKGROUND: Cardiovascular diseases (CVDs) rank amongst the leading causes of long-term disability and mortality. Predicting CVD risk and identifying associated genes are crucial for prevention, early intervention, and drug discovery. The recent availability of UK Biobank Proteomics data enables investigation of blood proteins and their association with a variety of diseases. We sought to predict 10 year CVD risk using this data modality and known CVD risk factors.

Authors

  • Héctor Climente-González
    Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, The Innovation Building, Roosevelt Dr, Headington, Oxford, OX3 7FZ, United Kingdom. HECG@novonordisk.com.
  • Min Oh
    Virginia Tech University, Dep. of Computer Science, Blacksburg, VA 24061, USA.
  • Urszula Chajewska
    Microsoft Corporation, 14820 NE 36th St, Redmond, WA, 98052, USA.
  • Roya Hosseini
    Microsoft Corporation, 14820 NE 36th St, Redmond, WA, 98052, USA.
  • Sudipto Mukherjee
    Microsoft Corporation, 14820 NE 36th St, Redmond, WA, 98052, USA.
  • Wei Gan
    College of Computer Science, Sichuan University, Chengdu, 610064, People's Republic of China.
  • Matthew Traylor
    Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, The Innovation Building, Roosevelt Dr, Headington, Oxford, OX3 7FZ, United Kingdom.
  • Sile Hu
    Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, The Innovation Building, Roosevelt Dr, Headington, Oxford, OX3 7FZ, United Kingdom.
  • Ghazaleh Fatemifar
    Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, The Innovation Building, Roosevelt Dr, Headington, Oxford, OX3 7FZ, United Kingdom.
  • Jonas Ghouse
    University of Copenhagen, 2200, Copenhagen N, Denmark.
  • Paul Pangilinan Del Villar
    Microsoft Corporation, 14820 NE 36th St, Redmond, WA, 98052, USA.
  • Erik Vernet
    Digital Science & Innovation, Novo Nordisk A/S, Maaloev, Denmark.
  • Nils Koelling
    Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, The Innovation Building, Roosevelt Dr, Headington, Oxford, OX3 7FZ, United Kingdom.
  • Liang Du
  • Robin Abraham
    Microsoft Corporation, 14820 NE 36th St, Redmond, WA, 98052, USA.
  • Chuan Li
    State Key Laboratory for Molecular Virology and Genetic Engineering, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Joanna M M Howson
    Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, The Innovation Building, Roosevelt Dr, Headington, Oxford, OX3 7FZ, United Kingdom.

Keywords

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