Author

Fauve Vergauwe

Ghent University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 1

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Andrea M SassiD· 1 shared Callum J. HighmoreiD· 1 shared Filip Van NieuwerburghiD· 1 shared Gaetan De WaeleiD· 1 shared Jeremy Stephen WebbiD· 1 shared Mads LichtenbergiD· 1 shared Margo C. CnockaertiD· 1 shared Niall HanrahaniD· 1 shared

Activity by Year

2025-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperMicrobiology

Andrea M SassORCID

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1 paper

Recent·Most cited

Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms

Fauve Vergauwe, Gaetan De Waele, Andrea M Sass, Callum J. Highmore, Niall Hanrahan, Yoshiki Cook, Mads Lichtenberg, Margo C. Cnockaert, Peter A. R. Vandamme, Sumeet Mahajan, Jeremy Stephen Webb, Filip Van Nieuwerburgh, Thomas Bjarnsholt, Willem Waegeman, Tom Coenye

The paper uses machine learning to predict tobramycin susceptibility in Pseudomonas aeruginosa biofilms using multiple analytical methods, achieving high accuracy with MALDI-TOF MS and Raman spectroscopy.

bioRxiv · Apr 24, 2025 · PreprintMicrobiology