Faecal microbiome-based machine learning for multi-class disease diagnosis
Here, using fecal metagenomics data of 2,320 individuals, the authors develop a microbiome-based machine learning approach showing high accuracy for multi-class disease diagnosis, highlighting its potential application in improving noninvasive diagnostics and monitor responses to therapy.
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-34405-3 |
| _version_ | 1817970214195691520 |
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| author | Qi Su Qin Liu Raphaela Iris Lau Jingwan Zhang Zhilu Xu Yun Kit Yeoh Thomas W. H. Leung Whitney Tang Lin Zhang Jessie Q. Y. Liang Yuk Kam Yau Jiaying Zheng Chengyu Liu Mengjing Zhang Chun Pan Cheung Jessica Y. L. Ching Hein M. Tun Jun Yu Francis K. L. Chan Siew C. Ng |
| author_facet | Qi Su Qin Liu Raphaela Iris Lau Jingwan Zhang Zhilu Xu Yun Kit Yeoh Thomas W. H. Leung Whitney Tang Lin Zhang Jessie Q. Y. Liang Yuk Kam Yau Jiaying Zheng Chengyu Liu Mengjing Zhang Chun Pan Cheung Jessica Y. L. Ching Hein M. Tun Jun Yu Francis K. L. Chan Siew C. Ng |
| author_sort | Qi Su |
| collection | DOAJ |
| description | Here, using fecal metagenomics data of 2,320 individuals, the authors develop a microbiome-based machine learning approach showing high accuracy for multi-class disease diagnosis, highlighting its potential application in improving noninvasive diagnostics and monitor responses to therapy. |
| first_indexed | 2024-04-13T20:31:08Z |
| format | Article |
| id | doaj.art-4d13f20fc0364cfc832e975f3b7201d3 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-13T20:31:08Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-4d13f20fc0364cfc832e975f3b7201d32022-12-22T02:31:10ZengNature PortfolioNature Communications2041-17232022-11-011311810.1038/s41467-022-34405-3Faecal microbiome-based machine learning for multi-class disease diagnosisQi Su0Qin Liu1Raphaela Iris Lau2Jingwan Zhang3Zhilu Xu4Yun Kit Yeoh5Thomas W. H. Leung6Whitney Tang7Lin Zhang8Jessie Q. Y. Liang9Yuk Kam Yau10Jiaying Zheng11Chengyu Liu12Mengjing Zhang13Chun Pan Cheung14Jessica Y. L. Ching15Hein M. Tun16Jun Yu17Francis K. L. Chan18Siew C. Ng19Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Department of Medicine and Therapeutics, The Chinese University of Hong KongMicrobiota I-Center (MagIC)Microbiota I-Center (MagIC)Department of Medicine and Therapeutics, The Chinese University of Hong KongMicrobiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Microbiota I-Center (MagIC)Department of Medicine and Therapeutics, The Chinese University of Hong KongMicrobiota I-Center (MagIC)Microbiota I-Center (MagIC)Here, using fecal metagenomics data of 2,320 individuals, the authors develop a microbiome-based machine learning approach showing high accuracy for multi-class disease diagnosis, highlighting its potential application in improving noninvasive diagnostics and monitor responses to therapy.https://doi.org/10.1038/s41467-022-34405-3 |
| spellingShingle | Qi Su Qin Liu Raphaela Iris Lau Jingwan Zhang Zhilu Xu Yun Kit Yeoh Thomas W. H. Leung Whitney Tang Lin Zhang Jessie Q. Y. Liang Yuk Kam Yau Jiaying Zheng Chengyu Liu Mengjing Zhang Chun Pan Cheung Jessica Y. L. Ching Hein M. Tun Jun Yu Francis K. L. Chan Siew C. Ng Faecal microbiome-based machine learning for multi-class disease diagnosis Nature Communications |
| title | Faecal microbiome-based machine learning for multi-class disease diagnosis |
| title_full | Faecal microbiome-based machine learning for multi-class disease diagnosis |
| title_fullStr | Faecal microbiome-based machine learning for multi-class disease diagnosis |
| title_full_unstemmed | Faecal microbiome-based machine learning for multi-class disease diagnosis |
| title_short | Faecal microbiome-based machine learning for multi-class disease diagnosis |
| title_sort | faecal microbiome based machine learning for multi class disease diagnosis |
| url | https://doi.org/10.1038/s41467-022-34405-3 |
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