Objective
GENOMED4ALL will support the pooling of genomic, clinical data and other “-omics” health data (data EHR, PET, MRI and CT , Next Generation Sequencing, Microarray, Genome Wide Association, Copy Number Variations, DNA sequencing, RNA sequencing, including single cell, etc.) through a secure and privacy respectful data sharing platform based on the novel Federated Learning scheme, to advance research in personalised medicine in haematological diseases thanks to advanced novel AI models and standardized sharing of cross-border data. GENOMED4ALL will make use of the existing infrastructures and initiatives, including powerful High Performance Computing facilities, hospital registries, data processing tools, and pre-existing repositories, starting from 10 clinical partners repositories to be enlarged especially by the resources provided by ERN-EuroBloodNet where GENOMED4ALL clinical partners have a leading position, which contain 66 relevant clinical sites providing repositories and knowledge, for the successful exploitation of genomics, clinical and other related “-omics” data to facilitate personalised medicine in common, rare and ultrarare haematological diseases to demonstrate the versatility and utility of the solutions, and 20 external of this network.
GENOMED4ALL will demonstrate the potential and benefits of trustable and explainable AI technologies, with a novel approach to AI models and algorithms using AI advanced deep learning, variational autoencoders, generative models, besides combining with advanced statistical and Machine learning processes approaches to exploit the powerful set of “-omics” data which will be at researchers’ disposal. This will allow for identifying new knowledge, to support clinical research and decision making by linking Europe's relevant genomic repositories in haematological diseases, while ensuring full compliance with data protection legislation and ethical principles, and increasing the AI trust for personalized medicine.
Field of science
- /natural sciences/computer and information sciences/computer security/data protection
- /natural sciences/computer and information sciences/data science/data processing
- /humanities/philosophy, ethics and religion/ethics/ethical principles
- /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning
Programme(s)
Call for proposal
H2020-SC1-FA-DTS-2020-1
See other projects for this call
Funding Scheme
RIA - Research and Innovation actionCoordinator
28040 Madrid
Spain
Participants (22)
64293 Darmstadt
28232 Madrid
00138 Roma
08860 Castelldefel
1853 Strombeek-bever
20100 Rozzano (Mi)
08035 Barcelona
75000 Paris
81377 Munchen
3584 CX Utrecht
47007 Valladolid
1683 Ayios Dometios
35122 Padova
04109 Leipzig
10561 Athina
40126 Bologna
1165 Kobenhavn
08003 Barcelona
10124 Torino
70013 Irakleio
75382 Paris
40033 Casalecchio Di Reno Bo