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Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks

Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-...

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Autores principales: Vilne, Baiba, Meistere, Irēna, Grantiņa-Ieviņa, Lelde, Ķibilds, Juris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6691741/
https://www.ncbi.nlm.nih.gov/pubmed/31447800
http://dx.doi.org/10.3389/fmicb.2019.01722
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author Vilne, Baiba
Meistere, Irēna
Grantiņa-Ieviņa, Lelde
Ķibilds, Juris
author_facet Vilne, Baiba
Meistere, Irēna
Grantiņa-Ieviņa, Lelde
Ķibilds, Juris
author_sort Vilne, Baiba
collection PubMed
description Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.
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spelling pubmed-66917412019-08-23 Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks Vilne, Baiba Meistere, Irēna Grantiņa-Ieviņa, Lelde Ķibilds, Juris Front Microbiol Microbiology Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches. Frontiers Media S.A. 2019-08-06 /pmc/articles/PMC6691741/ /pubmed/31447800 http://dx.doi.org/10.3389/fmicb.2019.01722 Text en Copyright © 2019 Vilne, Meistere, Grantiņa-Ieviņa and Ķibilds. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Vilne, Baiba
Meistere, Irēna
Grantiņa-Ieviņa, Lelde
Ķibilds, Juris
Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks
title Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks
title_full Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks
title_fullStr Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks
title_full_unstemmed Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks
title_short Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks
title_sort machine learning approaches for epidemiological investigations of food-borne disease outbreaks
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6691741/
https://www.ncbi.nlm.nih.gov/pubmed/31447800
http://dx.doi.org/10.3389/fmicb.2019.01722
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