Human Exposome Assessment Platform
Description: HEAP will provide a research resource for the integrated and efficient analysis of the human exposome. One primary outcome of the project is the complete process for obtaining actionable knowledge from the following sustainable cohorts: 1) a nation-wide Maternity Cohort where the impact of exposures on the health of women and children is supported by measures from wearable exposure sensors and metabolomics analysis, 2) a large cervical screening cohort enabling studies on women´s health supported with systematic epigenomics and metagenomics analyses, 3) and the systematic collection of consumer purchasing receipts linked to health outcomes will open new possibilities for assessing the impact of exposures on health in households. HEAP will launch a state-of-the-science informatics infrastructure (IaaS) composed of prime ICT resources from partner institutions in several EU countries, accessed through a software platform (PaaS) that includes the world’s fastest Hadoop platform to provide data warehousing and applied AI. The platform customised for exposome assessment will provide a decision support system for researchers, policymakers, and industry. All the data managed and processed by HEAP will be available for future research as well as the developed analyses. The platform can be enriched with new data and analysis pipelines and can be deployed in multiple instances to create a HEAP network towards producing shareable and reusable knowledge to promote health in society. HEAP will be under the umbrella of the European Human Exposome Network and will actively commit to collaboration and sharing of results and outcomes as well as contribute to i) a common ethical and regulatory framework, ii) common standardization and FAIRness of exposome data and iii) common dissemination strategies and efforts.
Funder: European Commission
Amount: €12,000,000
Funding type: research
Funder scheme: H2020 - RIA
Start date: 2020-01-01
End date: 2025-06-30
Institution: KAROLINSKA INSTITUTET
Funded outputs count: 67
Funder award ID: 874662
OpenAlex ID: G3005032613