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A European standardization framework for data integration and data-driven in silico models for personalized medicine

Project description

Integrating Big Data for predictive computational models in personalised medicine

Technological advances in biomedical sciences over the years have led to the production of a large amount of omics, imaging, health and lifestyle data. Although this Big Data holds immense potential for clinical applications, it has not been successfully exploited. The key objective of the EU-funded EU-STANDS4PM project is to establish a pan-European expert forum for evaluating existing standards and developing universal guidelines for the harmonised integration of heterogeneous health data for in silico methodologies. The project's efforts will assist in the provision of guidelines for verifying and validating predictive computational models that can be applied in personalised medicine approaches.

Objective

The capacity to generate data in Life Sciences and health research with modern omics and imaging technologies has increased many orders of magnitude in the last decade. In combination with patient/personal derived data such as electronic health records, patient registries and -databases, as well as life style information this Big Data holds an immense potential for clinical applications, especially for in silico personalized medicine approaches. However, and despite the ever progressing technological advances in producing data, the exploitation of Big Data information to generate new knowledge for medical benefits, while guaranteeing data privacy and security, is lacking behind its full potential. A reason for this obstacle is the inherent heterogeneity of Big Data and the lack of broadly accepted standards that allow interoperable integration of heterogeneous health data to perform analysis and interpretation for predictive in silico modelling approaches in health research such as personalized medicine. Further obstacles are legal issues surrounding the use of personal data. To overcome these obstacles, we will establish a pan-European Expert forum with two main objectives: (i) to assess and evaluate national standardization strategies for interoperable health data integration (such as omics-, disease-focused-, clinical-/treatment- or healthcare- and socioeconomic-/lifestyle-data) as well as data-driven in silico modelling approaches and (ii) to harmonize and develop universal (cross-border) standards as well as recommendations for in silico methodologies applied in personalized medicine approaches. This pan-European Expert Forum —the EU-STANDS4PM consortium— has the overarching aim to bundle transnational standardization guidelines for in silico methodologies in transnational and clinical research to unfold the potential of personalized medicine.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

CSA - Coordination and support action

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) H2020-SC1-BHC-2018-2020

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Coordinator

FORSCHUNGSZENTRUM JULICH GMBH
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 489 590,00
Address
WILHELM JOHNEN STRASSE
52428 JULICH
Germany

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Region
Nordrhein-Westfalen Köln Düren
Activity type
Research Organisations
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 489 590,00

Participants (16)

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