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 Valéria Batista, Suffys, Philip Noel, Conceição, Emilyn Costa
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.
id IEC-2_4216b74d8883b3a7ef79dbb84d5e8b1f
oai_identifier_str oai:patua.iec.gov.br:iec/4534
network_acronym_str IEC-2
network_name_str Repositório Digital do Instituto Evandro Chagas (Patuá)
repository_id_str
spelling 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
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 Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Digital do Instituto Evandro Chagas (Patuá)
instname:Instituto Evandro Chagas (IEC)
instacron:IEC
instname_str Instituto Evandro Chagas (IEC)
instacron_str IEC
institution IEC
reponame_str Repositório Digital do Instituto Evandro Chagas (Patuá)
collection Repositório Digital do Instituto Evandro Chagas (Patuá)
bitstream.url.fl_str_mv https://patua.iec.gov.br/bitstreams/3b9b89bc-abfa-4b4e-90d1-0459b3596e3a/download
https://patua.iec.gov.br/bitstreams/e0b43f26-a302-4533-9552-318bee16f7db/download
https://patua.iec.gov.br/bitstreams/8a5112df-934c-4770-9b65-4407f4f55ead/download
https://patua.iec.gov.br/bitstreams/445bc4e9-e0cb-4b89-af73-f81c8968d992/download
bitstream.checksum.fl_str_mv a361e5be09964820abacb54e01786760
11832eea31b16df8613079d742d61793
aa4d619e8b0d32951564435e3cf76eea
fd8a38ed7c4e1793b7634da6c307728d
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositório Digital do Instituto Evandro Chagas (Patuá) - Instituto Evandro Chagas (IEC)
repository.mail.fl_str_mv clariceneta@iec.gov.br || Biblioteca@iec.gov.br
_version_ 1809190038204841984