A biochemically-interpretable machine learning classifier for microbial GWAS
Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical ef...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-16310-9 |
| _version_ | 1818421229752680448 |
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| author | Erol S. Kavvas Laurence Yang Jonathan M. Monk David Heckmann Bernhard O. Palsson |
| author_facet | Erol S. Kavvas Laurence Yang Jonathan M. Monk David Heckmann Bernhard O. Palsson |
| author_sort | Erol S. Kavvas |
| collection | DOAJ |
| description | Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles. |
| first_indexed | 2024-12-14T13:07:03Z |
| format | Article |
| id | doaj.art-975d3eec652849ec8a3f9bfe2f5ad9c0 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-14T13:07:03Z |
| publishDate | 2020-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-975d3eec652849ec8a3f9bfe2f5ad9c02022-12-21T23:00:18ZengNature PortfolioNature Communications2041-17232020-05-0111111110.1038/s41467-020-16310-9A biochemically-interpretable machine learning classifier for microbial GWASErol S. Kavvas0Laurence Yang1Jonathan M. Monk2David Heckmann3Bernhard O. Palsson4Department of Bioengineering, University of CaliforniaDepartment of Chemical Engineering, Queen’s UniversityDepartment of Bioengineering, University of CaliforniaDepartment of Bioengineering, University of CaliforniaDepartment of Bioengineering, University of CaliforniaCurrent machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles.https://doi.org/10.1038/s41467-020-16310-9 |
| spellingShingle | Erol S. Kavvas Laurence Yang Jonathan M. Monk David Heckmann Bernhard O. Palsson A biochemically-interpretable machine learning classifier for microbial GWAS Nature Communications |
| title | A biochemically-interpretable machine learning classifier for microbial GWAS |
| title_full | A biochemically-interpretable machine learning classifier for microbial GWAS |
| title_fullStr | A biochemically-interpretable machine learning classifier for microbial GWAS |
| title_full_unstemmed | A biochemically-interpretable machine learning classifier for microbial GWAS |
| title_short | A biochemically-interpretable machine learning classifier for microbial GWAS |
| title_sort | biochemically interpretable machine learning classifier for microbial gwas |
| url | https://doi.org/10.1038/s41467-020-16310-9 |
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