Author

Florian Seefried

Technical University of MunichORCID 0000-0003-2334-6541Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Biochemistry· 1 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:proteomics· 2 Research Focus:clustering· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Bernhard KüsteriD· 2 shared Daniel Paul ZolgiD· 2 shared Mathias WilhelmiD· 2 shared Alexander HogrebeiD· 1 shared Andreas F. R. HühmeriD· 1 shared Armin SoleymaniniyaiD· 1 shared Bernard DelangheiD· 1 shared Florian P BayeriD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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2 shared papersBioinformatics

Bernhard KüsterORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintNov 7, 2025

Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM

Wassim Gabriel, Daniel Paul Zolg, Victor Giurcoiu…·Mathias Wilhelm

This paper introduces Prosit-PTM, a deep learning model for PTM discovery that improves zero-shot prediction using a synthetic dataset and chemically-informed encoding.

DOI 10.1101/2025.11.07.687302

proteomicsdeep learning

𝕏 PostBluesky

BiochemistryNature MethodsPeer-reviewedApr 22, 2025

Unifying the analysis of bottom-up proteomics data with CHIMERYS

Martin Frejno, Michelle T. Berger, Johanna Tüshaus…·Mathias Wilhelm

CHIMERYS deconvolves chimeric spectra in proteomic data, enabling unified analysis across DDA, DIA, and PRM experiments by accurately identifying and quantifying multiple peptides per spectrum.

46 citationstop 1%

proteomicsdeep learning

𝕏 PostBluesky