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Machine Learning Frontiers in Precision Medicine

Machine Learning Frontiers in Precision Medicine

Objective

Healthcare is entering the digital era: More and more patient data, from the molecular level of genome sequences to the level of image phenotypes and health history, are available in digital form. Exploring this big health data promises to reveal new insights into disease mechanisms and therapy outcomes. Ultimately, the goal is to exploit these insights for Precision Medicine, which hopes to offer personalized preventive care and therapy selection for each patient.
A technology with transformational potential in analysing this health data is Machine Learning. Machine Learning strives to discover new knowledge in form of statistical dependencies in large datasets. Mining health data is, however, not a simple direct application of established machine learning techniques. On the contrary, the emerging population-scale and ultra-high dimensionality of health data creates the need to develop Machine Learning algorithms that can successfully operate at this scale. Overcoming these frontiers in Machine Learning is key to making the vision of Precision Medicine a reality.
To meet this challenge, Europe urgently needs a new generation of scientists with knowledge in both machine learning and in health data analysis, who are extremely rare at a global scale. Our ETN’s goal is to close this gap, by bringing together leading European research institutes in Machine Learning and Statistical Genetics, both from the private and public sector, to train 14 early stage researchers. These scientists will help to shape the future of this important topic and increase Europe’s competitiveness in this domain, which will have severe academic and industrial impact in the future and has the potential to shape the healthcare and high tech sector in Europe in the 21st century.

Coordinator

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Address

Raemistrasse 101
8092 Zuerich

Switzerland

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 562 553,28

Participants (12)

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UNIVERSITE DE LIEGE

Belgium

EU Contribution

€ 256 320

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV

Germany

EU Contribution

€ 505 576,80

SIEMENS HEALTHCARE GMBH

Germany

EU Contribution

€ 252 788,40

TARTU ULIKOOL

Estonia

EU Contribution

€ 232 069,68

STACC OU

Estonia

EU Contribution

€ 232 069,68

FUNDACION PUBLICA ANDALUZA PROGRESO Y SALUD

Spain

EU Contribution

€ 250 904,88

UNIVERSIDAD CARLOS III DE MADRID

Spain

EU Contribution

€ 250 904,88

ASSOCIATION POUR LA RECHERCHE ET LE DEVELOPPEMENT DES METHODES ET PROCESSUS INDUSTRIELS

France

EU Contribution

€ 274 802,04

UNIVERSITE PARIS DIDEROT - PARIS 7

France

QLUCORE AB

Sweden

EU Contribution

€ 281 982,96

IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD

Israel

EU Contribution

€ 263 500,92

UNIVERSITE PARIS DESCARTES

France

EU Contribution

€ 274 802,04

Partners (4)

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TECHNISCHE UNIVERSITAET MUENCHEN

UNIVERSITE DE RECHERCHE PARIS SCIENCES ET LETTRES - PSL RESEARCH UNIVERSITY

PHARMATICS LIMITED

ROCHE DIAGNOSTICS GMBH

Project information

Grant agreement ID: 813533

Status

Ongoing project

  • Start date

    1 January 2019

  • End date

    31 December 2022

Funded under:

H2020-EU.1.3.1.

  • Overall budget:

    € 3 638 275,56

  • EU contribution

    € 3 638 275,56

Coordinated by:

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Switzerland