Overview

In the recent years, precision Medicine (PM) has led to an improved approach for patient healthcare which promises the ability to diagnose and cure illnesses on an individual basis, and in a targeted way. The integration of different sets of data that originate from the same physical source extending from their genetic background to the environment and their lifestyle is the basis of the PM and leads to the more effective treatment with less adverse events and unnecessary costs. PM remains a promising field but is severely hampered by the necessary data access restrictions. Machine learning is an approach for knowledge/information extraction which has several successful applications in diagnosis and prevention (ref), prognosis and therapy decision making

Objective 1

Align and connect different national Precision Medicine initiatives and Rare Disease repositories, adhering both to the expected security and ethical requirements as well as the relevant national regulations, towards a unified query interface

Objective 2

Specify a reusable protocol and reference implementation for making data models findable, accessible, interoperable and reproducible (FAIR) for humans and machines, aiming to support cross-repository queries and ML through data models rather than data itself

Objective 3

Demonstrate the value and impact of the protocol through an application of a collective ML approach based on combined phenotypic and omics data across repositories, towards higher resolution patient stratification and outcome prediction

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Participants

Members of the consortium

Centre for Research and Technology Hellas

Research Centre

Università Degli Studi di Padova

University

Karolinska Institutet

Research Centre

University Hospital Heidelberg

University

European Molecular Biology Laboratory (for ELIXIR)

Research centre

Centre National de la Recherche Scientifique

Research Centre

Universitat Zurich

University

Leiden University Medical Center

University

Fundacio Centre de Regulacio Genomica

Research Centre

National Center for Scientific Research “Demokritos”

Research Centre

Stichting Duchenne Data Foundation

Non profit organization

Munich Leukemia Laboratory

SME

Université Libre de Bruxelles”

University

University of Tartu

University

Technical University of Munich

University

Barcelona Supercomputing Center

Research Centre

Athens Technology Center

SME

Machine learning, collective learning, precision medicine, Chronic lymphocytic leukemia, lung cancer, Duchenne muscular dystrophy, Intellectual Disability, privacy-by-design

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