Author

Julia-Stefanie Frick

Technical University of MunichORCID 0000-0002-7662-7956Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Organism:E. coli· 1 Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:protein structure prediction· 1 Assay:structural biology· 1

Frequent Co-Authors

Abdelhakim BoudriouaiD· 1 shared Alexander BartholomäusiD· 1 shared Alexander DiltheyiD· 1 shared Alexander GoesmanniD· 1 shared Alexander N.R. WeberiD· 1 shared András SzolekiD· 1 shared Andreas DiefenbachiD· 1 shared Andreas KelleriD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperMicrobiology

Abdelhakim BoudriouaORCID

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2 papers

Recent·Most cited

Crystal structure of E. coli Nissle 1917 flagellin reveals novel features that modulate bacterial motility but not TLR5 recognition

Johanna Jakob, Michael B. Braun, Katharina Hipp, Iris Koch, Gaopeng Li, Pascal Felgner, María Giralt-Zúñiga, Hannah Raasch, Corinna Ghering-Khav, András Szolek, Abdelhakim Boudrioua, Thomas Hagemann, Samuel Wagner, Thilo Stehle, Liudmila Andreeva, , Alexander N.R. Weber

This study determines the crystal structure of E. coli Nissle 1917 flagellin and shows that hypervariable regions affect motility but not TLR5 recognition.

bioRxiv · Apr 14, 2026 · PreprintMicrobiology

Swarm Learning as a privacy-preserving machine learning approach for disease classification

Stefanie Warnat‐Herresthal, Hartmut Schultze, Krishnaprasad Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N. Ahmad Aziz, Sofia Ira Ktena, Florian Tran, Michael Bitzer, Stephan Ossowski, Nicolas Casadei, , Alexander Goesmann

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

Nature · May 26, 2021Peer-reviewedBioinformatics