A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic.

Journal: JCO clinical cancer informatics
Published Date: Apr 1, 2019

Abstract

PURPOSE: Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation.

Authors

  • Manuela Salvucci
    Royal College of Surgeons in Ireland, Dublin, Ireland.
  • Arman Rahman
    OncoMark, Dublin, Ireland.
  • Alexa J Resler
    Royal College of Surgeons in Ireland, Dublin, Ireland.
  • Girish M Udupi
    OncoMark, Dublin, Ireland.
  • Deborah A McNamara
    Beaumont Hospital, Dublin, Ireland.
  • Elaine W Kay
    Beaumont Hospital, Dublin, Ireland.
  • Pierre Laurent-Puig
    Université Paris Descartes, Paris, France.
  • Daniel B Longley
    Queen's University Belfast, Belfast, United Kingdom.
  • Patrick G Johnston
    Queen's University Belfast, Belfast, United Kingdom.
  • Mark Lawler
    Queen's University Belfast, Belfast, United Kingdom.
  • Richard Wilson
    Queen's University Belfast, Belfast, United Kingdom.
  • Manuel Salto-Tellez
    Integrated Pathology Unit, Division of Molecular Pathology, The Institute of Cancer Research London and The Royal Marsden NHS Foundation Trust, Sutton, United Kingdom.
  • Sandra Van Schaeybroeck
    Queen's University Belfast, Belfast, United Kingdom.
  • Mairin Rafferty
    OncoMark, Dublin, Ireland.
  • William M Gallagher
    OncoMark, Dublin, Ireland.
  • Markus Rehm
    Royal College of Surgeons in Ireland, Dublin, Ireland.
  • Jochen H M Prehn
    Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Centre for Systems Medicine, Dublin 2, Ireland.

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