Publikationen

Autor	Pezoulas, V.C.; Goules, A.; Kalatzis, F.; Chatzis, L.; Kourou, K.D.; Venetsanopoulou, A.; Exarchos, T.P.; Gandolfo, S.; Votis, K.; Zampeli, E.; Burmeister, J.; May, T.; Pérez, M.M.; Lishchuk, I.; Chondrogiannis, T.; Andronikou, V.; Varvarigou, T.; Filipovic, N.; Tsiknakis, M.; Baldini, C.; Bombardieri, M.; Bootsma, H.; Bowman, S.J.; Shahnawaz Soyfoo, M.; Parisis, D.; Delporte, C.; Devauchelle-Pensec, V.; Pers, J.-O.; Dörner, T.; Bartoloni, E.; Gerli, R.; Giacomelli, R.; Jonsson, R.; Ng, W.-F.; Priori, R.; Ramos-Casals, M.; Sivils, K.; Skopouli, F.; Torsten, W.; Roon, J.A.G. van; Xavier, M.; Vita, S. de; Tzioufas, A.G.; Fotiadis, D.I.
Datum	2022
Art	Journal Article
Abstrakt	For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity a long with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs.
Url	https://publica.fraunhofer.de/handle/publica/416207