Author

Thomas Pichl

Technical University of MunichORCID 0000-0001-9185-9387Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Microbiology· 2

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Organism:E. coli· 1 Organism:human· 1 Assay:metagenomics· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Janko SattleriD· 2 shared Karsten Michael BorgwardtiD· 2 shared Lucas MirandaiD· 2 shared Adrian EgliiD· 1 shared Andreas WendeliD· 1 shared Axel G. HamprechtiD· 1 shared Dexiong CheniD· 1 shared Ela SauerborniD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersMicrobiology

Janko SattlerORCID

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2 papers

Recent·Most cited

MicrobiologybioRxivPreprintJul 13, 2026

Machine learning-guided discovery of a conserved plasmid proteomic signature enables MALDI-TOF MS detection of pOXA-48-carrying Enterobacterales

Janko Sattler, Johannes B. Mueller-Reif, Dexiong Chen…·Karsten Michael Borgwardt

The paper develops a machine learning model to detect pOXA-48-carrying Enterobacterales using MALDI-TOF MS spectra, achieving high accuracy across different cohorts.

DOI 10.64898/2026.07.12.738056

humanproteomics

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MicrobiologyJournal of Global Antimicrobial ResistancePeer-reviewedMar 20, 2026

Early detection of ampicillin susceptibility in Enterococcus faecium with MALDI-TOF MS and machine learning

Thomas Pichl, Lucas Miranda, Matthias Tobias Warkotsch…·Janko Sattler

This paper uses machine learning on MALDI-TOF mass spectrometry data to detect ampicillin susceptibility in Enterococcus faecium, achieving high accuracy across datasets.

1 citation

E. colimetagenomics

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