Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms

Alexander Mazein(Université Claude Bernard Lyon 1), Marek Ostaszewski(University of Luxembourg), Inna Kuperstein(Inserm), Steven Watterson(University of Ulster), Nicolas Le Novère(Babraham Institute), Diane Lefaudeux(Université Claude Bernard Lyon 1), Bertrand De Meulder(Université Claude Bernard Lyon 1), Johann Pellet(Université Claude Bernard Lyon 1), Irina Balaur(Université Claude Bernard Lyon 1), Mansoor Saqi(Université Claude Bernard Lyon 1), Maria Manuela Nogueira(Université Claude Bernard Lyon 1), Feng He, Andrew Parton(University of Ulster), Nicolas Lemonnier(Centre National de la Recherche Scientifique), Piotr Gawron(University of Luxembourg), Stephan Gebel(University of Luxembourg), Pierre Hainaut(Centre National de la Recherche Scientifique), Markus Ollert(University of Southern Denmark), Uğur Doğrusöz(Bilkent University), Emmanuel Barillot(Inserm), Andreï Zinovyev(Inserm), Reinhard Schneider(University of Luxembourg), Rudi Balling(University of Luxembourg), Charles Auffray(Université Claude Bernard Lyon 1)
npj Systems Biology and Applications
May 30, 2018
Cited by 128Open Access
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Abstract

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.


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