DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis
Rattachement africain : gb, cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
MOTIVATION: Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. RESULTS: We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. AVAILABILITY AND IMPLEMENTATION: The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé, mais le titre doit être comparé manuellement.
- Titre Crossref
- DeepAMR for predicting co-occurrent resistance of <i>Mycobacterium tuberculosis</i>
- Date Crossref
- 28/01/2019
- Éditeur
- Oxford University Press (OUP)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Oxford Institute of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Suzhou Research Institute pays non établi dans la noticeStructure de recherche
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John Radcliffe Hospital pays non établi dans la noticeÉtablissement de santé
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Public Health England pays non établi dans la noticeOrganisme public
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Oxford-Suzhou Centre for Advanced Research pays non établi dans la noticeInstitution
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NIHR Oxford Biomedical Research Centre pays non établi dans la noticeStructure de recherche
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National Infection Service pays non établi dans la noticeInstitution
Institute of Biomedical Engineering — University of Oxford, Suzhou Research Institute et John Radcliffe Hospital, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.