Yang, Y;
Walker, TM;
Walker, AS;
Wilson, DJ;
Peto, TEA;
Crook, DW;
Shamout, F;
... Clifton, DA; + view all
(2019)
DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis.
Bioinformatics
10.1093/bioinformatics/btz067.
(In press).
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Abstract
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. Methods: We used a large cohort of TB patients from 16 countries across six continents where wholegenome 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. Results: 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 visualisation shows that DeepAMR_cluster captures lineage-related clusters in the latent space.
| Type: | Article |
|---|---|
| Title: | DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/bioinformatics/btz067 |
| Publisher version: | https://doi.org/10.1093/bioinformatics/btz067 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10066305 |
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