Privacy-preserving Artificial Intelligence Techniques in Biomedicine. - Reihaneh Torkzadehmahani; Reza Nasirigerdeh; David B. Blumenthal; Tim Kacprowski; Markus List; Julian O. Matschinske; Julian Späth; Nina Kerstin Wenke; Béla Bihari; Tobias Frisch; Anne Hartebrodt; Anne-Christin Hauschild; et al. (CoRR, 2020)

The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond. - Julian O. Matschinske; Julian Späth; Reza Nasirigerdeh; Reihaneh Torkzadehmahani; Anne Hartebrodt; Balázs Orbán; Sándor Fejér; Olga I. Zolotareva; Mohammad Bakhtiari; Béla Bihari; Marcus D. Bloice; Nina C. Donner; et al. (CoRR, 2021)

Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies. - Gihanna Galindez; Julian O. Matschinske; Tim D. Rose; Sepideh Sadegh; Marisol Salgado-Albarrán; Julian Späth; Jan Baumbach; Josch Konstantin Pauling (Nat. Comput. Sci., 2021)

dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning. - Han Cao 001; Youcheng Zhang; Jan Baumbach; Paul R. Burton; Dominic B. Dwyer; Nikolaos Koutsouleris; Julian O. Matschinske; Yannick Marcon; Sivanesan Rajan; Thilo Rieg; Patricia Ryser-Welch; Julian Späth; et al. (Bioinform., 2022)

Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt. - Yuliya Burankova; Miriam Abele; Mohammad Bakhtiari; Christine von Törne; Teresa Barth; Lisa Schweizer; Pieter Giesbertz; Johannes R. Schmidt; Stefan Kalkhof; Janina Müller-Deile; Peter A. van Veelen; Yassene Mohammed; et al. (CoRR, 2024)