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Project description

Synthetic data for advanced personalised medicine

Traditional data-sharing platforms that focus on overcoming technical barriers create challenges for the healthcare industry and the research community in developing tools for personalised healthcare using explainable AI. The EU-funded SEARCH project aims to address European biomedical challenges and advance personalised medicine by enabling comprehensive data aggregation and analysis while protecting the integrity and privacy of original datasets through synthetic data proxies. It will tackle legal, ownership, and subject privacy concerns, specifically targeting distributed institutional repositories that are hesitant to share multimodal clinical data. The project will combine clinical synthetic data proxies with a federated learning framework to overcome security concerns, promote public-private data collaborations, create synthetic datasets for evaluating biomedical AI solutions, and drive innovation in digital healthcare.

Objective

The objectives of SEARCH are truly ground-breaking, seeking to enable extensive data aggregation and analysis while safeguarding the integrity and privacy of original datasets through synthetically derived proxies. This initiative is designed to address biomedical challenges in Europe and offer translational solutions that will ultimately contribute to the advancement of personalised medicine. Unlike traditional data-sharing platforms that mainly focus on technical obstacles, SEARCH adopts an innovative approach by addressing legal, ownership, and subject privacy concerns. It specifically targets distributed institutional repositories that are hesitant to share multimodal clinical data, overcoming security concerns through a combination of clinical synthetic data proxies and a Federated Learning framework. Until synthetic data proxies gain wider acceptance, this combined strategy aimed at alleviating security concerns, facilitates the scalability required for AI analysis and promotes creative public-private data collaborations.

SEARCH will offer advanced data federation capabilities, incorporating unique Synthetic Data Generation features to create various data types, including those not comprehensively addressed currently (e.g. wearable device data, image sequences, and genomic data). Through curated access to these novel digital tools, SEARCH will facilitate convenient access for the healthcare industry and research community to address bottlenecks and challenges in the development of novel tools for personalized prevention, diagnosis and treatment based on explainable AI. Moreover, SEARCH will provide agreed-upon gold standard synthetic datasets for evaluating the performance of biomedical AI solutions. SEARCH aims to consolidate European Innovation and Research endeavours by promoting public and private collaborations to unlock the potential for innovation in the digital healthcare sector.

Fields of science (EuroSciVoc)

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-JU-RIA - HORIZON JU Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-JU-IHI-2023-05

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Coordinator

THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD, OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 883 226,25
Address
COLLEGE GREEN TRINITY COLLEGE
D02 CX56 Dublin
Ireland

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Region
Ireland Eastern and Midland Dublin
Activity type
Higher or Secondary Education Establishments
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 883 226,25

Participants (24)

Partners (5)

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