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 |
| _version_ | 1817976541429104640 |
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| author | Anna G. Green Chang Ho Yoon Michael L. Chen Yasha Ektefaie Mack Fina Luca Freschi Matthias I. Gröschel Isaac Kohane Andrew Beam Maha Farhat |
| author_facet | Anna G. Green Chang Ho Yoon Michael L. Chen Yasha Ektefaie Mack Fina Luca Freschi Matthias I. Gröschel Isaac Kohane Andrew Beam Maha Farhat |
| author_sort | Anna G. Green |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T22:04:56Z |
| format | Article |
| id | doaj.art-324ced25c6614875b873dd6824eb5f41 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-13T22:04:56Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-324ced25c6614875b873dd6824eb5f412022-12-22T02:27:58ZengNature PortfolioNature Communications2041-17232022-07-0113111210.1038/s41467-022-31236-0A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosisAnna G. Green0Chang Ho Yoon1Michael L. Chen2Yasha Ektefaie3Mack Fina4Luca Freschi5Matthias I. Gröschel6Isaac Kohane7Andrew Beam8Maha Farhat9Department of Biomedical Informatics, Harvard Medical SchoolDepartment of Biomedical Informatics, Harvard Medical SchoolDepartment of Biomedical Informatics, Harvard Medical SchoolDepartment of Biomedical Informatics, Harvard Medical SchoolHarvard CollegeDepartment of Biomedical Informatics, Harvard Medical SchoolDepartment of Biomedical Informatics, Harvard Medical SchoolDepartment of Biomedical Informatics, Harvard Medical SchoolDepartment of Biomedical Informatics, Harvard Medical SchoolDepartment of Biomedical Informatics, Harvard Medical SchoolPathogen 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.https://doi.org/10.1038/s41467-022-31236-0 |
| spellingShingle | Anna G. Green Chang Ho Yoon Michael L. Chen Yasha Ektefaie Mack Fina Luca Freschi Matthias I. Gröschel Isaac Kohane Andrew Beam Maha Farhat A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis Nature Communications |
| title | A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis |
| title_full | A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis |
| title_fullStr | A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis |
| title_full_unstemmed | A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis |
| title_short | A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis |
| title_sort | convolutional neural network highlights mutations relevant to antimicrobial resistance in mycobacterium tuberculosis |
| url | https://doi.org/10.1038/s41467-022-31236-0 |
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