Cargando…

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

(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 B. melitensis, B. suis and B....

Descripción completa

Detalles Bibliográficos
Autores principales: Dematheis, Flavia, Walter, Mathias C., Lang, Daniel, Antwerpen, Markus, Scholz, Holger C., Pfalzgraf, Marie-Theres, Mantel, Enrico, Hinz, Christin, Wölfel, Roman, Zange, Sabine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416640/
https://www.ncbi.nlm.nih.gov/pubmed/36014076
http://dx.doi.org/10.3390/microorganisms10081658
_version_ 1784776527980789760
author Dematheis, Flavia
Walter, Mathias C.
Lang, Daniel
Antwerpen, Markus
Scholz, Holger C.
Pfalzgraf, Marie-Theres
Mantel, Enrico
Hinz, Christin
Wölfel, Roman
Zange, Sabine
author_facet Dematheis, Flavia
Walter, Mathias C.
Lang, Daniel
Antwerpen, Markus
Scholz, Holger C.
Pfalzgraf, Marie-Theres
Mantel, Enrico
Hinz, Christin
Wölfel, Roman
Zange, Sabine
author_sort Dematheis, Flavia
collection PubMed
description (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 B. melitensis, B. suis and B. abortus to identify the optimal classification method within popular supervised and unsupervised machine learning (ML) algorithms. (2) Methods: A consensus feature selection strategy was applied to pinpoint from among the 500 MS features those that yielded the best ML model and that may play a role in species differentiation. Unsupervised k-means and hierarchical agglomerative clustering were evaluated using the silhouette coefficient, while the supervised classifiers Random Forest, Support Vector Machine, Neural Network, and Multinomial Logistic Regression were explored in a fine-tuning manner using nested k-fold cross validation (CV) with a feature reduction step between the two CV loops. (3) Results: Sixteen differentially expressed peaks were identified and used to feed ML classifiers. Unsupervised and optimized supervised models displayed excellent predictive performances with 100% accuracy. The suitability of the consensus feature selection strategy for learning system accuracy was shown. (4) Conclusion: A meaningful ML approach is here introduced, to enhance Brucella spp. classification using MALDI-TOF MS data.
format Online
Article
Text
id pubmed-9416640
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-94166402022-08-27 Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis Dematheis, Flavia Walter, Mathias C. Lang, Daniel Antwerpen, Markus Scholz, Holger C. Pfalzgraf, Marie-Theres Mantel, Enrico Hinz, Christin Wölfel, Roman Zange, Sabine Microorganisms Article (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 B. melitensis, B. suis and B. abortus to identify the optimal classification method within popular supervised and unsupervised machine learning (ML) algorithms. (2) Methods: A consensus feature selection strategy was applied to pinpoint from among the 500 MS features those that yielded the best ML model and that may play a role in species differentiation. Unsupervised k-means and hierarchical agglomerative clustering were evaluated using the silhouette coefficient, while the supervised classifiers Random Forest, Support Vector Machine, Neural Network, and Multinomial Logistic Regression were explored in a fine-tuning manner using nested k-fold cross validation (CV) with a feature reduction step between the two CV loops. (3) Results: Sixteen differentially expressed peaks were identified and used to feed ML classifiers. Unsupervised and optimized supervised models displayed excellent predictive performances with 100% accuracy. The suitability of the consensus feature selection strategy for learning system accuracy was shown. (4) Conclusion: A meaningful ML approach is here introduced, to enhance Brucella spp. classification using MALDI-TOF MS data. MDPI 2022-08-17 /pmc/articles/PMC9416640/ /pubmed/36014076 http://dx.doi.org/10.3390/microorganisms10081658 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dematheis, Flavia
Walter, Mathias C.
Lang, Daniel
Antwerpen, Markus
Scholz, Holger C.
Pfalzgraf, Marie-Theres
Mantel, Enrico
Hinz, Christin
Wölfel, Roman
Zange, Sabine
Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis
title Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis
title_full Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis
title_fullStr Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis
title_full_unstemmed Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis
title_short Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related Species Brucella melitensis, Brucella abortus and Brucella suis
title_sort machine learning algorithms for classification of maldi-tof ms spectra from phylogenetically closely related species brucella melitensis, brucella abortus and brucella suis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416640/
https://www.ncbi.nlm.nih.gov/pubmed/36014076
http://dx.doi.org/10.3390/microorganisms10081658
work_keys_str_mv AT dematheisflavia machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT waltermathiasc machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT langdaniel machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT antwerpenmarkus machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT scholzholgerc machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT pfalzgrafmarietheres machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT mantelenrico machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT hinzchristin machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT wolfelroman machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis
AT zangesabine machinelearningalgorithmsforclassificationofmalditofmsspectrafromphylogeneticallycloselyrelatedspeciesbrucellamelitensisbrucellaabortusandbrucellasuis