Tuberculosis drug resistance profiling based on machine learning: A literature review

Detalhes bibliográficos
Autor(a) principal: Sharma, Abhinav
Data de Publicação: 2022
Outros Autores: Machado, Edson, Lima, Karla Valeria Batista, Suffys, Philip, Conceição, Emilyn Costa
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/54391
Resumo: Faculty of Engineering and Technology, Liverpool John Moores University (LJMU). Liverpool, United Kingdom.
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spelling Sharma, AbhinavMachado, EdsonLima, Karla Valeria BatistaSuffys, PhilipConceição, Emilyn Costa2022-08-04T11:40:13Z2022-08-04T11:40:13Z2022SHARMA, Abhinav et al. Tuberculosis drug resistance profiling based on machine learning: A literature review. The Brazilian Journal of Infectious Diseases, v. 26, n. 1, 102332, p. 1-9, Feb. 2022.1413-8670https://www.arca.fiocruz.br/handle/icict/5439110.1016/j.bjid.2022.102332engSociedade Brasileira de InfectologiaMycobacterium tuberculosisSequenciamento completo do genomaPrevisão de resistência a medicamentosAprendizado de máquinaMycobacterium tuberculosisWhole genome sequencingDrug resistance predictionMachine LearningTuberculosis drug resistance profiling based on machine learning: A literature reviewinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFaculty of Engineering and Technology, Liverpool John Moores University (LJMU). Liverpool, United Kingdom.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz, Laboratório de Biologia Molecular Aplicada a Micobactéerias. Rio de Janeiro, RJ, Brasil.Instituto Evandro Chagas. Seção de Bacteriologia e Micologia. Ananindeua, PA, Brasil / Universidade do Estado do Pará, Instituto de Ciências Biológicas e da Saúde, Pós Graduação em Biologia Parasitária na Amazônia, Belém, PA, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz, Laboratório de Biologia Molecular Aplicada a Micobactérias. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Programa de Pós-graduação em Pesquisa Clínica e Doenças Infecciosas. Rio de Janeiro, RJ, Brasil / Department of Science and Innovation - National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is one of the top 10 causes of death worldwide. Drug-resistant tuberculosis (DR-TB) poses a major threat to the World Health Organization’s “End TB” strategy which has defined its target as the year 2035. In 2019, there were close to 0.5 million cases of DRTB, of which 78% were resistant to multiple TB drugs. The traditional culture-based drug susceptibility test (DST - the current gold standard) often takes multiple weeks and the necessary laboratory facilities are not readily available in low-income countries. Whole genome sequencing (WGS) technology is rapidly becoming an important tool in clinical and research applications including transmission detection or prediction of DR-TB. For the latter, many tools have recently been developed using curated database(s) of known resistance conferring mutations. However, documenting all the mutations and their effect is a time-taking and a continuous process and therefore Machine Learning (ML) techniques can be useful for predicting the presence of DR-TB based on WGS data. This can pave the way to an earlier detection of drug resistance and consequently more efficient treatment when compared to the traditional DST.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/54391/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALPhilip_Suffys_etal_IOC_2022.pdfPhilip_Suffys_etal_IOC_2022.pdfapplication/pdf736789https://www.arca.fiocruz.br/bitstream/icict/54391/2/Philip_Suffys_etal_IOC_2022.pdf92ec9dbbe70bd8833831ddf22b92ee9cMD52icict/543912023-03-02 08:53:17.201oai:www.arca.fiocruz.br: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ório InstitucionalPUBhttps://www.arca.fiocruz.br/oai/requestrepositorio.arca@fiocruz.bropendoar:21352023-03-02T11:53:17Repositório Institucional da FIOCRUZ (ARCA) - Fundação Oswaldo Cruz (FIOCRUZ)false
dc.title.pt_BR.fl_str_mv Tuberculosis drug resistance profiling based on machine learning: A literature review
title Tuberculosis drug resistance profiling based on machine learning: A literature review
spellingShingle Tuberculosis drug resistance profiling based on machine learning: A literature review
Sharma, Abhinav
Mycobacterium tuberculosis
Sequenciamento completo do genoma
Previsão de resistência a medicamentos
Aprendizado de máquina
Mycobacterium tuberculosis
Whole genome sequencing
Drug resistance prediction
Machine Learning
title_short Tuberculosis drug resistance profiling based on machine learning: A literature review
title_full Tuberculosis drug resistance profiling based on machine learning: A literature review
title_fullStr Tuberculosis drug resistance profiling based on machine learning: A literature review
title_full_unstemmed Tuberculosis drug resistance profiling based on machine learning: A literature review
title_sort Tuberculosis drug resistance profiling based on machine learning: A literature review
author Sharma, Abhinav
author_facet Sharma, Abhinav
Machado, Edson
Lima, Karla Valeria Batista
Suffys, Philip
Conceição, Emilyn Costa
author_role author
author2 Machado, Edson
Lima, Karla Valeria Batista
Suffys, Philip
Conceição, Emilyn Costa
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Sharma, Abhinav
Machado, Edson
Lima, Karla Valeria Batista
Suffys, Philip
Conceição, Emilyn Costa
dc.subject.other.pt_BR.fl_str_mv Mycobacterium tuberculosis
Sequenciamento completo do genoma
Previsão de resistência a medicamentos
Aprendizado de máquina
topic Mycobacterium tuberculosis
Sequenciamento completo do genoma
Previsão de resistência a medicamentos
Aprendizado de máquina
Mycobacterium tuberculosis
Whole genome sequencing
Drug resistance prediction
Machine Learning
dc.subject.en.pt_BR.fl_str_mv Mycobacterium tuberculosis
Whole genome sequencing
Drug resistance prediction
Machine Learning
description Faculty of Engineering and Technology, Liverpool John Moores University (LJMU). Liverpool, United Kingdom.
publishDate 2022
dc.date.accessioned.fl_str_mv 2022-08-04T11:40:13Z
dc.date.available.fl_str_mv 2022-08-04T11:40:13Z
dc.date.issued.fl_str_mv 2022
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv SHARMA, Abhinav et al. Tuberculosis drug resistance profiling based on machine learning: A literature review. The Brazilian Journal of Infectious Diseases, v. 26, n. 1, 102332, p. 1-9, Feb. 2022.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/54391
dc.identifier.issn.pt_BR.fl_str_mv 1413-8670
dc.identifier.doi.none.fl_str_mv 10.1016/j.bjid.2022.102332
identifier_str_mv SHARMA, Abhinav et al. Tuberculosis drug resistance profiling based on machine learning: A literature review. The Brazilian Journal of Infectious Diseases, v. 26, n. 1, 102332, p. 1-9, Feb. 2022.
1413-8670
10.1016/j.bjid.2022.102332
url https://www.arca.fiocruz.br/handle/icict/54391
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Sociedade Brasileira de Infectologia
publisher.none.fl_str_mv Sociedade Brasileira de Infectologia
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
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institution FIOCRUZ
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collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/54391/1/license.txt
https://www.arca.fiocruz.br/bitstream/icict/54391/2/Philip_Suffys_etal_IOC_2022.pdf
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