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@ARTICLE{Burankova:280783,
      author       = {Burankova, Yuliya and Abele, Miriam and Bakhtiari, Mohammad
                      and von Toerne, Christine and Barth, Teresa K and Schweizer,
                      Lisa and Giesbertz, Pieter and Schmidt, Johannes R and
                      Kalkhof, Stefan and Müller-Deile, Janina and van Veelen,
                      Peter A and Mohammed, Yassene and Hammer, Elke and Arend,
                      Lis and Adamowicz, Klaudia and Laske, Tanja and Hartebrodt,
                      Anne and Frisch, Tobias and Meng, Chen and Matschinske,
                      Julian and Späth, Julian and Röttger, Richard and
                      Schwämmle, Veit and Hauck, Stefanie M and Lichtenthaler,
                      Stefan F and Imhof, Axel and Mann, Matthias and Ludwig,
                      Christina and Kuster, Bernhard and Baumbach, Jan and
                      Zolotareva, Olga},
      title        = {{P}rivacy-preserving multicenter differential protein
                      abundance analysis with {F}ed{P}rot.},
      journal      = {Nature computational science},
      volume       = {5},
      number       = {8},
      issn         = {2662-8457},
      address      = {London},
      publisher    = {Nature Research},
      reportid     = {DZNE-2025-00967},
      pages        = {675 - 688},
      year         = {2025},
      abstract     = {Quantitative mass spectrometry has revolutionized
                      proteomics by enabling simultaneous quantification of
                      thousands of proteins. Pooling patient-derived data from
                      multiple institutions enhances statistical power but raises
                      serious privacy concerns. Here we introduce FedProt, the
                      first privacy-preserving tool for collaborative differential
                      protein abundance analysis of distributed data, which
                      utilizes federated learning and additive secret sharing. In
                      the absence of a multicenter patient-derived dataset for
                      evaluation, we created two: one at five centers from E. coli
                      experiments and one at three centers from human serum.
                      Evaluations using these datasets confirm that FedProt
                      achieves accuracy equivalent to the DEqMS method applied to
                      pooled data, with completely negligible absolute differences
                      no greater than 4 × 10-12. By contrast, -log10P computed by
                      the most accurate meta-analysis methods diverged from the
                      centralized analysis results by up to 25-26.},
      keywords     = {Humans / Proteomics: methods / Mass Spectrometry /
                      Escherichia coli: metabolism / Privacy / Algorithms},
      cin          = {AG Lichtenthaler},
      ddc          = {004},
      cid          = {I:(DE-2719)1110006},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40646319},
      pmc          = {pmc:PMC12374843},
      doi          = {10.1038/s43588-025-00832-7},
      url          = {https://pub.dzne.de/record/280783},
}