Author

Matteo Pilz

University of TübingenORCID 0000-0002-7470-0806Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:proteomics· 2 Approach:deep learning· 1 Organism:human· 1 Assay:metagenomics· 1 Research Focus:software / tool· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Timo T. SachsenbergiD· 2 shared Alina BaueriD· 1 shared Annika NeldeiD· 1 shared Axel WalteriD· 1 shared Florian HuberiD· 1 shared Hans‐Georg RammenseeiD· 1 shared Jens BaueriD· 1 shared Jonas ScheidiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersBioinformatics

Timo T. SachsenbergORCID

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2 papers

Recent·Most cited

Deep learning enables direct HLA typing from immunopeptidomics data

Matteo Pilz, Jonas Scheid, Alina Bauer, Steffen Lemke, Timo T. Sachsenberg, Jens Bauer, Annika Nelde, Josua Stadelmaier, Axel Walter, Hans‐Georg Rammensee, Sven Nahnsen, Oliver Kohlbacher, Juliane Sarah Walz

This paper introduces Immunotype, a deep learning model that directly types HLA alleles from immunopeptidomics data with 87.2% accuracy.

bioRxiv · Apr 10, 2026 · PreprintBioinformatics

Unified and standardized mass spectrometry data processing in Python using spectrum_utils

Wout Bittremieux, Lev I. Levitsky, Matteo Pilz, Timo T. Sachsenberg, Florian Huber, Mingxun Wang, Pieter C. Dorrestein

The paper updates the spectrum_utils Python package to integrate mass spectrometry standards, enhance data processing, and unify visualization across proteomics and metabolomics applications.

Journal of Proteome Research · Jan 23, 2023Peer-reviewedBioinformatics