Project description
Research training network in machine learning for precision healthcare
Healthcare is becoming digital with more and more patient data, from the molecular level to the health history of individuals, being available in electronic form. Linking human genetic variation with phenotypic traits on a population scale would dramatically improve the understanding of disease mechanisms and hence pave the way for personalised preventive care and therapy. Due to the enormous amounts of data to analyse, successfully finding associations between genetic characteristics and corresponding phenotypic traits requires powerful computational tools. Funded by the Marie Skłodowska-Curie Actions programme, the MLFPM2018 network of leading European research institutes in machine learning and statistical genetics trains 14 early-stage researchers to become a new generation of scientific experts to meet the challenges of the new digitalised era in healthcare.
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.
Fields of science
- natural sciencescomputer and information sciencesdata science
- natural sciencesbiological sciencesgenetics
- medical and health scienceshealth sciencespersonalized medicine
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Coordinator
8092 Zuerich
Switzerland
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Participants (12)
4000 Liege
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80539 Munchen
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91052 Erlangen
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51005 Tartu
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51003 Tartu
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
41092 Sevilla
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28903 Getafe (Madrid)
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75272 Paris
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Participation ended
75006 Paris
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223 70 Lund
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
49527 Petach Tikva
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75006 Paris
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Partners (4)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
80333 Muenchen
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
75006 Paris
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Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
EH16 4UX EDINBURGH
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
68305 Mannheim
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