Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species <i>Brucella melitensis</i>, <i>Brucella abortus</i> and <i>Brucella suis</i>

Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species <i>Brucella melitensis</i>, <i>Brucella abortus</i> and <i>Brucella suis</i>

(1) Background: MALDI-TOF mass spectrometry (MS) is the gold standard for microbial fingerprinting, however, for phylogenetically closely related species, the resolution power drops down to the genus level. In this study, we analyzed MALDI-TOF spectra from 44 strains of <i>B. melitensis</i&...

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Bibliographic Details
Main Authors: Flavia Dematheis, Mathias C. Walter, Daniel Lang, Markus Antwerpen, Holger C. Scholz, Marie-Theres Pfalzgraf, Enrico Mantel, Christin Hinz, Roman Wölfel, Sabine Zange
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Microorganisms
Subjects:
MALDI-TOF MS
<i>Brucella melitensis</i>
<i>B. suis</i>
<i>B. abortus</i>
machine learning
nested <i>k</i>-fold cross validation
Online Access:https://www.mdpi.com/2076-2607/10/8/1658

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