Cargando…
Artificial Intelligence Models for Zoonotic Pathogens: A Survey
Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the fa...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607465/ https://www.ncbi.nlm.nih.gov/pubmed/36296187 http://dx.doi.org/10.3390/microorganisms10101911 |
_version_ | 1784818551128850432 |
---|---|
author | Pillai, Nisha Ramkumar, Mahalingam Nanduri, Bindu |
author_facet | Pillai, Nisha Ramkumar, Mahalingam Nanduri, Bindu |
author_sort | Pillai, Nisha |
collection | PubMed |
description | Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens. |
format | Online Article Text |
id | pubmed-9607465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96074652022-10-28 Artificial Intelligence Models for Zoonotic Pathogens: A Survey Pillai, Nisha Ramkumar, Mahalingam Nanduri, Bindu Microorganisms Review Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens. MDPI 2022-09-27 /pmc/articles/PMC9607465/ /pubmed/36296187 http://dx.doi.org/10.3390/microorganisms10101911 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 | Review Pillai, Nisha Ramkumar, Mahalingam Nanduri, Bindu Artificial Intelligence Models for Zoonotic Pathogens: A Survey |
title | Artificial Intelligence Models for Zoonotic Pathogens: A Survey |
title_full | Artificial Intelligence Models for Zoonotic Pathogens: A Survey |
title_fullStr | Artificial Intelligence Models for Zoonotic Pathogens: A Survey |
title_full_unstemmed | Artificial Intelligence Models for Zoonotic Pathogens: A Survey |
title_short | Artificial Intelligence Models for Zoonotic Pathogens: A Survey |
title_sort | artificial intelligence models for zoonotic pathogens: a survey |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607465/ https://www.ncbi.nlm.nih.gov/pubmed/36296187 http://dx.doi.org/10.3390/microorganisms10101911 |
work_keys_str_mv | AT pillainisha artificialintelligencemodelsforzoonoticpathogensasurvey AT ramkumarmahalingam artificialintelligencemodelsforzoonoticpathogensasurvey AT nanduribindu artificialintelligencemodelsforzoonoticpathogensasurvey |