Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning.

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Bibliographic Details
Title: Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning.
Authors: Duroux D; ETH AI Center, ETH Zurich, Zurich, Switzerland. diane.duroux@ai.ethz.ch.; Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. diane.duroux@ai.ethz.ch.; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland. diane.duroux@ai.ethz.ch.; SIB Swiss Institute of Bioinformatics, Basel, Switzerland. diane.duroux@ai.ethz.ch., Yang Y; Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland., Sattler J; Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany., Krauthammer M; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland., Egli A; Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland.
Source: Scientific reports [Sci Rep] 2026 Jun 02. Date of Electronic Publication: 2026 Jun 02.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-026-53519-y