NEXT GENERATION TOOLS FOR GENOME-CENTRIC MULTIMODAL DATA INTEGRATION IN PERSONALISED CARDIOVASCULAR MEDICINE

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Forschungsstelle

SBFI

Projektnummer

23.00577

Projekttitel

NEXT GENERATION TOOLS FOR GENOME-CENTRIC MULTIMODAL DATA INTEGRATION IN PERSONALISED CARDIOVASCULAR MEDICINE

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Abstract (Englisch)	Healthcare is the fasted growing EU27 expenditure. Personalised medicine, comprising tailored approaches for prevention, diagnosis,monitoring and treatment is essential to reduce the burden of disease and improve the quality of life. Integration of multiple data types(multimodal data) into artificial intelligence models is required for the development of accurate and personalised interventions. Thisis particularly true for the inclusion of genomic data, which is information-rich and individual-specific, and more routinely availableas the cost of sequencing continues to fall. Multimodal data integration is complex due to privacy & governance requirements, thepresence of multiple standards, distinct data formats, and underlying data complexity and volume. NextGen tools will remove barriersin data integration several cardiovascular use cases. NextGen deliverables will include tooling for multimodal data integration andresearch portability, extension of secure federated analytics to genomic computation, more effective federated learning over distributedinfrastructures, more effective and accessible tools for genomic data analysis; improved clinical efficiency of variant prioritisation;scalable genomic data curation; and improved data discoverability and data management. A comprehensive gap analysis of the existinglandscape, factoring ongoing initiatives will ensure NextGen deliverables are forward-looking and complementary. NextGen embeddedgovernance framework and robust regulatory processes will ensure secure multi-jurisdictional multiomic multimodal data access alignedwith initiatives including “1+ Million Genomes” and the European Health Data Space. Several real-world pilots will demonstrate theeffectiveness of NextGen tools and will be integrated in the NextGen Pathfinder network of five collaborating clinical sites as a selfcontaineddata ecosystem and comprehensive proof of concept.

Kategorie

Text

Abstract
(Englisch)

Healthcare is the fasted growing EU27 expenditure. Personalised medicine, comprising tailored approaches for prevention, diagnosis,monitoring and treatment is essential to reduce the burden of disease and improve the quality of life. Integration of multiple data types(multimodal data) into artificial intelligence models is required for the development of accurate and personalised interventions. Thisis particularly true for the inclusion of genomic data, which is information-rich and individual-specific, and more routinely availableas the cost of sequencing continues to fall. Multimodal data integration is complex due to privacy & governance requirements, thepresence of multiple standards, distinct data formats, and underlying data complexity and volume. NextGen tools will remove barriersin data integration several cardiovascular use cases. NextGen deliverables will include tooling for multimodal data integration andresearch portability, extension of secure federated analytics to genomic computation, more effective federated learning over distributedinfrastructures, more effective and accessible tools for genomic data analysis; improved clinical efficiency of variant prioritisation;scalable genomic data curation; and improved data discoverability and data management. A comprehensive gap analysis of the existinglandscape, factoring ongoing initiatives will ensure NextGen deliverables are forward-looking and complementary. NextGen embeddedgovernance framework and robust regulatory processes will ensure secure multi-jurisdictional multiomic multimodal data access alignedwith initiatives including “1+ Million Genomes” and the European Health Data Space. Several real-world pilots will demonstrate theeffectiveness of NextGen tools and will be integrated in the NextGen Pathfinder network of five collaborating clinical sites as a selfcontaineddata ecosystem and comprehensive proof of concept.