{"paper":{"title":"Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.QM","authors_text":"Anne Hartebrodt, Axel Imhof, Bernhard Kuster, Chen Meng, Christina Ludwig, Christine von T\\\"orne, Elke Hammer, Jan Baumbach, Janina M\\\"uller-Deile, Johannes R. Schmidt, Julian Matschinske, Julian Sp\\\"ath, Klaudia Adamowicz, Lis Arend, Lisa Schweizer, Matthias Mann, Miriam Abele, Mohammad Bakhtiari, Olga Zolotareva, Peter A van Veelen, Pieter Giesbertz, Richard R\\\"ottger, Stefanie M. Hauck, Stefan Kalkhof, Stefan Lichtenthaler, Tanja Laske, Teresa Barth, Tobias Frisch, Veit Schw\\\"ammle, Yassene Mohammed, Yuliya Burankova","submitted_at":"2024-07-21T17:09:20Z","abstract_excerpt":"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 significant 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 LFQ E.coli experiments and one at"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15220","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.15220/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}