EU-STANDS4PM: European framework for big data in personalised medicine
28 March 2019
A pan-European Expert Forum joins forces to tackle the complexity of big data integration for in silico methodologies in personalised medicine.
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 personalised medicine” launched activities with support by the European Commission Directorate-General for Research and Innovation. During the next three years EU-STANDS4PM will initiate an 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 personalised medicine across Europe.
From data to knowledge – standards for personalised 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 standardisation 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 harmonisation and integration approaches across multiple disciplines to truly strengthen the predictive potential of personalised medicine.
Professor Stephan Beck (UCL) is a long-standing advocate of sharing data more openly and will lead the consortium’s efforts concerning Data Sharing and Governance which are critical for the successful implementation of personalised medicine. “We are very excited to be part of EU-STANDS4PM and plan to develop innovative solutions for improved sharing of data that cannot be shared under open access," says Professor Beck.
Aims of EU-STANDS4PM
The EU-STANDSPM project seeks to assess and evaluate national standardisation strategies for interoperable health data integration, as well as data-driven in silico modelling, approaches for personalised medicine with the aim to bundle European standardisation efforts. A central goal is to harmonise and develop cross-border standards as well as recommendations and guidelines for in silico methodologies applied in personalised medicine to:
- facilitate a sustained use of Life Science data in clinical and health research.
- advise regulatory authorities on a broad adaptation of harmonised 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.
The EU-STANDS4PM consortium
The EU-STANDSPM consortium is composed 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
- Forschungszentrum Jülich GmbH/Project Management Jülich, Germany
- HITS gGmbH, Germany
- Federal Agency for Medicines and Health Products, Belgium
- German Institute for Standardization, 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
- EU-STANDS4PM website
- Medical Genomics Research Group - Prof Beck
- Professor Stephan Beck academic profile