A pan-European expert forum to tackle the complexity of big data in personalized medicine

0

On January 1st 2019 the Horizon2020 Coordinating and Support Action “EU-STANDS4PM – A European standardization framework for data integration and data-driven in silico models for personalized medicine” launched activities with support by the European Commission Directorate-General for Research and Innovation. During the next three years EU-STANDS4PM will initiate a EU-wide mapping process to assess and evaluate strategies for data-driven in silico modelling approaches. A central goal is to develop harmonized transnational standards, recommendations and guidelines that allow a broad application of predictive in silico methodologies in personalized medicine across Europe.

From data to knowledge – standards for personalized medicine

Despite the ever progressing technological advances in producing data in life sciences, health and clinical research, the exploitation of the underlying data information to generate new knowledge for medical benefits is lacking behind its full potential. A reason for this obstacle is the inherent heterogeneity of different data sources and the lack of broadly accepted standards. In addition, further obstacles are associated with legal and ethical issues surrounding the use of personal data across disciplines and borders. There is a clear multi-disciplinary need for broadly applicable transnational standardization guidelines compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico guided methodologies. Such standards as well as interoperable solutions will foster data harmonization and integration approaches across multiple disciplines to truly strengthen the predictive potential of personalized medicine.

Aims

EU-STANDSPM will assess and evaluate national standardization strategies for health data integration as well as data-driven in silico modelling approaches for personalized medicine with the aim to bundle European standardization efforts. A central goal is to harmonize and develop cross-border standards as well as recommendations and guidelines for in silico methodologies applied in personalized medicine to:

  • facilitate a sustained use of Life Science data in clinical and health research;
  • advise regulatory authorities on a broad adaptation of harmonized health data and standards in research, and industry;
  • enable FAIR principles (Findable, Accessible, Interoperable and Reproducible) as well as legal and ethical requirements for health data integration strategies;
  • accelerate growth of the European data-driven economy.

EU-STANDS4PM is an open network and seeks input from all relevant stakeholders that have an interest in advancing predictive in silico methodologies in personalized medicine through broadly applicable standards as well as coordinated procedures for integration and harmonization of heterogeneous health data. This will help to sustain the competitiveness of the European Research Area and ensure a leading role for the European personalized medicine community of stakeholders in the transition from current reactive medical practice to a data-driven and predictive medicine of the future.

Consortium

The EU-STANDSPM consortium consists of the following partner institutions:

  • Bayer AG, Germany
  • Christian-Albrechts-University Kiel, Germany
  • Erasmus Medical Center Rotterdam, The Netherlands
  • European Molecular Biology Laboratory/European Bioinformatics Institute, United Kingdom
  • Federal Agency for Medicines and Health Products, Belgium
  • Forschungszentrum Jülich GmbH/Project Management Jülich, Germany
  • German Institute for Standardization, Germany
  • HITS gGmbH, Germany
  • Karolinska Institute, Sweden
  • Qiagen, Germany
  • University College London, United Kingdom
  • University of Copenhagen, Denmark
  • University of Oxford, United Kingdom
  • University of Parma, Italy
  • University of Rostoch, Germany
  • Vilnius University, Lithuania

###

Media Contact
Marc Kirschner
[email protected]

Leave A Reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More