A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis
Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution to multi-drug resistance diagnosis. Here, the authors present two deep convolutional neural networks that predict the antibiotic resistance phenotypes of M. tuberculosis isolates.
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-31236-0 |
| Summary: | Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution to multi-drug resistance diagnosis. Here, the authors present two deep convolutional neural networks that predict the antibiotic resistance phenotypes of M. tuberculosis isolates. |
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| ISSN: | 2041-1723 |