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Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis
- Necati Esener ,
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Alexandre Maciel-Guerra ,
Roles Data curation, Formal analysis, Software, Visualization, Writing – original draft, Writing – review & editing
¶‡ co-first authors.
Affiliation University of Nottingham School of Computer Science, Jubilee Campus, Nottingham, United Kingdom
⨯ -
Katharina Giebel,
Roles Investigation, Writing – review & editing
Affiliation Quality Milk Management Services ltd, Easton, United Kingdom
⨯ -
Daniel Lea,
Roles Formal analysis
Affiliation Digital Research Service, University of Nottingham, Sutton Bonington, United Kingdom
⨯ -
Martin J. Green,
Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing
Affiliation University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington, United Kingdom
⨯ - Andrew J. Bradley,
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Tania Dottorini
Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing
* E-mail: tania.dottorini@nottingham.ac.uk
Affiliation University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington, United Kingdom
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https://orcid.org/0000-0003-1396-9438
Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis
- Necati Esener,
- Alexandre Maciel-Guerra,
- Katharina Giebel,
- Daniel Lea,
- Martin J. Green,
- Andrew J. Bradley,
- Tania Dottorini
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- Published: June 11, 2021
- https://doi.org/10.1371/journal.pcbi.1009108