Tuberculosis drug resistance profiling based on machine learning: a literature review
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Digital do Instituto Evandro Chagas (Patuá) |
Texto Completo: | https://patua.iec.gov.br/handle/iec/4534 |
Resumo: | 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. |
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Sharma, AbhinavMachado, EdsonLima, Karla Valéria BatistaSuffys, Philip NoelConceição, Emilyn Costa2022-06-01T17:52:20Z2022-06-01T17:52:20Z2022SHARMA, Abhinav et al. Tuberculosis drug resistance profiling based on machine learning: a literature review. Brazilian Journal of Infectious Diseases, v. 26, n. 1, p. 1-9, Jan./Feb. 2022.1678-439https://patua.iec.gov.br/handle/iec/453410.1016/j.bjid.2022.102332.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.Liverpool John Moores University. Faculty of Engineering and Technology. Liverpool, United KingdomFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Biologia Molecular Aplicada a Micobactérias. Rio de Janeiro, RJ, BrazilMinistério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil / Universidade do Estado do Pará. Instituto de Ciências Biológicas e da Saúde. Pós-Graduação em Biologia Parasitária na Amazônia. Belém, PA, BrazilFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Biologia Molecular Aplicada a Micobactérias. Rio de Janeiro, RJ, BrazilFundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Programa de Pós-graduação em Pesquisa Clínica e Doenças Infecciosas. Rio de Janeiro, RJ, Brazil / Stellenbosch University. Faculty of Medicine and Health Sciences. Division of Molecular Biology and Human Genetics. South African Medical Research Council Centre for Tuberculosis Research. Department of Science and Innovation - National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research. Cape Town, South AfricaengElsevierTuberculosis drug resistance profiling based on machine learning: a literature reviewinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleTuberculose / patologiaMycobacterium tuberculosis / efeitos dos fármacosTuberculose Resistente a Múltiplos MedicamentosResistência a MedicamentosSequenciamento Completo do Genoma / métodosinfo:eu-repo/semantics/openAccessreponame:Repositório Digital do Instituto Evandro Chagas (Patuá)instname:Instituto Evandro Chagas (IEC)instacron:IECORIGINALTuberculosis drug resistance profiling based on machine learning: a literature review.pdfTuberculosis drug resistance profiling based on machine learning: a literature review.pdfapplication/pdf791757https://patua.iec.gov.br/bitstreams/3b9b89bc-abfa-4b4e-90d1-0459b3596e3a/downloada361e5be09964820abacb54e01786760MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82182https://patua.iec.gov.br/bitstreams/e0b43f26-a302-4533-9552-318bee16f7db/download11832eea31b16df8613079d742d61793MD52TEXTTuberculosis drug resistance profiling based on machine learning: a literature review.pdf.txtTuberculosis drug resistance profiling based on machine learning: a literature review.pdf.txtExtracted texttext/plain52116https://patua.iec.gov.br/bitstreams/8a5112df-934c-4770-9b65-4407f4f55ead/downloadaa4d619e8b0d32951564435e3cf76eeaMD55THUMBNAILTuberculosis drug resistance profiling based on machine learning: a literature review.pdf.jpgTuberculosis drug resistance profiling based on machine learning: a literature review.pdf.jpgGenerated Thumbnailimage/jpeg5887https://patua.iec.gov.br/bitstreams/445bc4e9-e0cb-4b89-af73-f81c8968d992/downloadfd8a38ed7c4e1793b7634da6c307728dMD56iec/45342022-10-20 21:22:23.274oai:patua.iec.gov.br:iec/4534https://patua.iec.gov.brRepositório InstitucionalPUBhttps://patua.iec.gov.br/oai/requestclariceneta@iec.gov.br || Biblioteca@iec.gov.bropendoar:2022-10-20T21:22:23Repositório Digital do Instituto Evandro Chagas (Patuá) - Instituto Evandro Chagas (IEC)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 |
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 Tuberculose / patologia Mycobacterium tuberculosis / efeitos dos fármacos Tuberculose Resistente a Múltiplos Medicamentos Resistência a Medicamentos Sequenciamento Completo do Genoma / métodos |
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 Valéria Batista Suffys, Philip Noel Conceição, Emilyn Costa |
author_role |
author |
author2 |
Machado, Edson Lima, Karla Valéria Batista Suffys, Philip Noel Conceição, Emilyn Costa |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Sharma, Abhinav Machado, Edson Lima, Karla Valéria Batista Suffys, Philip Noel Conceição, Emilyn Costa |
dc.subject.decsPrimary.pt_BR.fl_str_mv |
Tuberculose / patologia Mycobacterium tuberculosis / efeitos dos fármacos Tuberculose Resistente a Múltiplos Medicamentos Resistência a Medicamentos Sequenciamento Completo do Genoma / métodos |
topic |
Tuberculose / patologia Mycobacterium tuberculosis / efeitos dos fármacos Tuberculose Resistente a Múltiplos Medicamentos Resistência a Medicamentos Sequenciamento Completo do Genoma / métodos |
description |
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. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-06-01T17:52:20Z |
dc.date.available.fl_str_mv |
2022-06-01T17:52:20Z |
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. Brazilian Journal of Infectious Diseases, v. 26, n. 1, p. 1-9, Jan./Feb. 2022. |
dc.identifier.uri.fl_str_mv |
https://patua.iec.gov.br/handle/iec/4534 |
dc.identifier.issn.-.fl_str_mv |
1678-439 |
dc.identifier.doi.-.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. Brazilian Journal of Infectious Diseases, v. 26, n. 1, p. 1-9, Jan./Feb. 2022. 1678-439 10.1016/j.bjid.2022.102332. |
url |
https://patua.iec.gov.br/handle/iec/4534 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Elsevier |
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Elsevier |
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reponame:Repositório Digital do Instituto Evandro Chagas (Patuá) instname:Instituto Evandro Chagas (IEC) instacron:IEC |
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IEC |
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Repositório Digital do Instituto Evandro Chagas (Patuá) |
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