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An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction.
Yang, Yang; Walker, Timothy M; Kouchaki, Samaneh; Wang, Chenyang; Peto, Timothy E A; Crook, Derrick W; Clifton, David A.
Afiliación
  • Yang Y; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK.
  • Walker TM; Oxford-Suzhou Centre for Advanced Research, Suzhou, 215123, China.
  • Kouchaki S; Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.
  • Wang C; Centre for vision, Speech, and Signal processing, University of Surrey, Guildford, UK.
  • Peto TEA; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK.
  • Crook DW; Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital Headley Way, OX3 9DU, Oxford, UK.
  • Clifton DA; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way Headington, OX3 9DU, Oxford, UK.
Brief Bioinform ; 22(6)2021 11 05.
Article en En | MEDLINE | ID: mdl-34414415
ABSTRACT
Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method-heterogeneous graph attention network ('HGAT-AMR')-to predict anti-tuberculosis (TB) drug resistance. The HGAT-AMR model is able to accommodate incomplete phenotypic profiles, as well as provide 'attention scores' of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is 'paying attention to' in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Farmacorresistencia Bacteriana / Mycobacterium tuberculosis / Antituberculosos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Farmacorresistencia Bacteriana / Mycobacterium tuberculosis / Antituberculosos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Reino Unido