Addressing the clinical unmet needs in primary Sjögren's Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

  • Vasileios C. Pezoulas
  • , Andreas Goules
  • , Fanis Kalatzis
  • , Luke Chatzis
  • , Konstantina D. Kourou
  • , Aliki Venetsanopoulou
  • , Themis P. Exarchos
  • , Saviana Gandolfo
  • , Konstantinos Votis
  • , Evi Zampeli
  • , Jan Burmeister
  • , Thorsten May
  • , Manuel Marcelino Pérez
  • , Iryna Lishchuk
  • , Thymios Chondrogiannis
  • , Vassiliki Andronikou
  • , Theodora Varvarigou
  • , Nenad Filipovic
  • , Manolis Tsiknakis
  • , Chiara Baldini
  • Michele Bombardieri, Hendrika Bootsma, Simon J. Bowman, Muhammad Shahnawaz Soyfoo, Dorian Parisis, Christine Delporte, Valérie Devauchelle-Pensec, Jacques Olivier Pers, Thomas Dörner, Elena Bartoloni, Roberto Gerli, Roberto Giacomelli, Roland Jonsson, Wan Fai Ng, Roberta Priori, Manuel Ramos-Casals, Kathy Sivils, Fotini Skopouli, Witte Torsten, Joel A. G. van Roon, Mariette Xavier, Salvatore De Vita, Athanasios G. Tzioufas, Dimitrios I. Fotiadis

Research output: Contribution to journalArticlepeer-review

Abstract

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 along 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.

Original languageEnglish
Pages (from-to)471-484
Number of pages14
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
Publication statusPublished - Jan 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Biomarkers
  • Data curation
  • Data harmonization
  • Data sharing
  • Federated AI
  • Lymphoma classification
  • Primary Sjögren's syndrome

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