Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases
Allergologists play a central role in building ML-ready resources. By ensuring rigorous clinical annotation, standardization of data, transparent methods, and independent validation, they can maximize the utility of OAD and CBD and their combination to accelerate progress toward precision allergy medicine.
Authors
- Julien Goret (ORCID: https://orcid.org/0000-0003-0469-169X)
- J. Corriger (ORCID: https://orcid.org/0000-0001-7465-1920)
- Yannick Chantran (ORCID: https://orcid.org/0000-0002-9778-0044)
- Aleix Arnau-Soler
Institutions
- Centre National de la Recherche Scientifique (FR)
- Mercy Medical Center (US)
- Université de Bordeaux (FR)
- Inserm (FR)
- Université Paris Cité (FR)
- Max Delbrück Center (DE)
- Sorbonne Université (FR)
- Sorbonne Paris Cité (FR)
- ImmunoGen (United States) (US)
- Assistance Publique – Hôpitaux de Paris (FR)
- Bordeaux Population Health (FR)
- Bipar (FR)
- Immungenetics (Germany) (DE)
- German Centre for Cardiovascular Research (DE)
- Société Française d'Allergologie (FR)
- Centre d'Immunologie et des Maladies Infectieuses (FR)
- Centre de Recherche Saint-Antoine (FR)
- Institut de Recherche pour le Développement (FR)
Publication Details
- Journal
- Current Opinion in Allergy and Clinical Immunology
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1097/aci.0000000000001143
- Primary Topic
- Allergic Rhinitis and Sensitization
- Type
- article
- Field-Weighted Citation Impact
- 0.00