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Machine Learning for Personalized Medicine

Objective

"Over the last decade, enormous progress has been made on recording the health state of an individual patient down to the molecular level of gene activity and genomic information – even sequencing a patient’s genome for less than 1000 dollars is no longer an unrealistic goal. However, the ultimate hope to use all this information for personalized medicine, that is to tailor medical treatment to the needs of an individual, remains largely unfulfilled.
To turn the vision of personalized medicine into reality, many methodological problems remain to be solved: there is a lack of methods that allow us to gain a causal understanding of the underlying disease mechanisms, including gene-gene and gene-environment interactions. Similarly, there is an urgent need for integration of the heterogeneous patient data currently available, for improved and robust biomarker discovery for disease diagnosis, prognosis and therapy outcome prediction.
The field of machine learning, which tries to detect patterns, rules and statistical dependencies in large datasets, has also witnessed dramatic progress over the last decade and has had a profound impact on the Internet. Amongst others, advanced methods for high-dimensional feature selection, causality inference, and data integration have been developed or are topics of current research. These techniques address many of the key methodological challenges that personalized medicine faces today and keep it from rising to the next level.

Despite this rich potential of machine learning in personalized medicine, its impact on data-driven medicine remains low, due to a lack of experts with knowledge in both machine learning and in statistical genetics. Our ITN aims 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."

Coordinator

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Address

Raemistrasse 101
8092 Zuerich

Switzerland

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 595 024,06

Administrative Contact

Karsten Borgwardt (Prof.)

Participants (10)

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SIEMENS AKTIENGESELLSCHAFT

Germany

EU Contribution

€ 230 336,60

THE UNIVERSITY OF SHEFFIELD

United Kingdom

EU Contribution

€ 293 324,38

PHARMATICS LIMITED

United Kingdom

EU Contribution

€ 258 919,88

UNIVERSITE DE LIEGE

Belgium

EU Contribution

€ 238 607,73

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

France

EU Contribution

€ 520 165,03

INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE

France

EU Contribution

€ 264 216,38

UNIVERSIDAD CARLOS III DE MADRID

Spain

EU Contribution

€ 234 949,34

FUNDACION DE LA COMUNIDAD VALENCIANA CENTRO DE INVESTIGACION PRINCIPEFELIPE

Spain

EU Contribution

€ 234 949,34

Sloan-Kettering Institute for Cancer Research CORPORATION

United States

EU Contribution

€ 240 675,50

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV

Germany

EU Contribution

€ 646 888,70

Project information

Grant agreement ID: 316861

Status

Closed project

  • Start date

    1 January 2013

  • End date

    31 December 2016

Funded under:

FP7-PEOPLE

  • Overall budget:

    € 3 758 056,90

  • EU contribution

    € 3 758 056,90

Coordinated by:

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Switzerland