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Improved MALDI-TOF MS based antimicrobial resistance prediction through hierarchical stratification
View ORCID ProfileCaroline Weis, View ORCID ProfileBastian Rieck, Sebastian Balzer, Aline Cuénod, Adrian Egli, View ORCID ProfileKarsten Borgwardt
doi: https://doi.org/10.1101/2022.04.13.488198
Caroline Weis
1Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Switzerland
2SIB Swiss Institute of Bioinformatics, Switzerland
Bastian Rieck
1Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Switzerland
2SIB Swiss Institute of Bioinformatics, Switzerland
Sebastian Balzer
1Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Switzerland
Aline Cuénod
3Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
4Division of Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
Adrian Egli
3Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland
4Division of Clinical Bacteriology and Mycology, University Hospital Basel, Basel, Switzerland
Karsten Borgwardt
1Machine Learning and Computational Biology Lab, D-BSSE, ETH Zurich, Switzerland
2SIB Swiss Institute of Bioinformatics, Switzerland
Posted April 14, 2022.
Improved MALDI-TOF MS based antimicrobial resistance prediction through hierarchical stratification
Caroline Weis, Bastian Rieck, Sebastian Balzer, Aline Cuénod, Adrian Egli, Karsten Borgwardt
bioRxiv 2022.04.13.488198; doi: https://doi.org/10.1101/2022.04.13.488198
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