C-ELIXIR.02: ELIXIR IMPACT: a Swiss-Czech partnership for Integrating Molecular, Pathogen And Clinical data with AI Technologies
Authors
Keywords
Artificial Intelligence · Data Interoperability · Federated Data Analysis · Semantic Integration · Biomedical Data
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The ELIXIR IMPACT project addresses key challenges limiting the effective use of biological and health data in biomedical research, personalised medicine, and public health. Despite rapid advances in artificial intelligence (AI), scientific progress is often hindered by fragmented data resources, limited interoperability, and restricted access to sensitive clinical information. Building on the complementary expertise of ELIXIR Czech Republic and ELIXIR Switzerland, the project establishes a strategic collaboration to strengthen the integration, accessibility, and secure use of biological and health-related data across Europe.
The project focuses on five main objectives: (i) improving interoperability of molecular, lipid, metabolite, and pathogen data through semantic integration and AI-powered knowledge graphs; (ii) developing innovative AI-based tools and natural language interfaces to enhance data discovery and usability; (iii) creating secure and scalable infrastructures for cross-border sharing and analysis of sensitive omics and clinical data in compliance with European regulatory frameworks; (iv) expanding research capacities through targeted training, workshops, and educational resources; and (v) reinforcing long-term Czech–Swiss scientific cooperation within the ELIXIR ecosystem by leveraging complementary expertise and infrastructure.
By combining internationally recognised data resources, advanced bioinformatics expertise, and state-of-the-art AI methodologies, ELIXIR IMPACT will accelerate biological discoveries and support data-driven healthcare innovation. The project is aligned with European initiatives such as EOSC and will contribute to strengthening Europe’s leadership in biological data management, AI-enabled knowledge integration, and secure data sharing. Its outcomes will benefit thousands of researchers, clinicians, and data scientists, ultimately improving disease research, public health preparedness, and personalised healthcare.
Co-authors: Owen Appleton, Valérie Barbié, Karel Berka, Alan Bridge, Vojtěch Bystrý, Christophe Dessimoz, Jakub Galgonek, Gert van Geest, Robert Pergl, Lucy Poveda, Ana Claudia Sima, Vojtěch Spiwok, Jiri Vondrášek
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