Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning-Reference-Cited by-同舟云学术

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Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning

Published:2024-01 Issue: Volume:410 Page:110491
ISSN:0168-1605
Container-title:International Journal of Food Microbiology
language:en
Short-container-title:International Journal of Food Microbiology

Author:

Gmeiner Alexander,Njage Patrick Murigu Kamau,Hansen Lisbeth Truelstrup,Aarestrup Frank M.,Leekitcharoenphon Pimlapas

Publisher

Elsevier BV

Subject

General Medicine,Microbiology,Food Science

Reference58 articles.

1. Annual Report on Zoonoses in Denmark 2018;Anonymous,2019

2. Machine learning to predict the source of campylobacteriosis using whole genome data;Arning;PLoS Genet,2021

3. Prediction of acquired antimicrobial resistance for multiple bacterial species using neural networks;Aytan-Aktug;Msystems,2020

4. Predicting antimicrobial resistance using partial genome alignments;Aytan-Aktug;Msystems,2021

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1. Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions;Food Research International;2024-07

2. Is the persistence of Listeria monocytogenes in food processing facilities and its resistance to pathogen intervention linked to its phylogeny?;Applied and Environmental Microbiology;2024-06-18

3. Harnessing artificial intelligence in microbial food safety: global progress and implications in the ASEAN region;International Journal of Food Science & Technology;2024-05-15

4. Ultrasensitive and Highly Selective Detection of Staphylococcus aureus at the Single-Cell Level Using Bacteria-Imprinted Polymer and Vancomycin-Conjugated MnO₂ Nanozyme;Analytical Chemistry;2024-05-08

5. ListPred: A predictive ML tool for virulence potential and disinfectant tolerance inListeria monocytogenes;2024-01-29

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