Nature Communications (Nov 2022)
Faecal microbiome-based machine learning for multi-class disease diagnosis
- 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
Affiliations
- Qi Su
- Microbiota I-Center (MagIC)
- Qin Liu
- Microbiota I-Center (MagIC)
- Raphaela Iris Lau
- Microbiota I-Center (MagIC)
- Jingwan Zhang
- Microbiota I-Center (MagIC)
- Zhilu Xu
- Microbiota I-Center (MagIC)
- Yun Kit Yeoh
- Microbiota I-Center (MagIC)
- Thomas W. H. Leung
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong
- Whitney Tang
- Microbiota I-Center (MagIC)
- Lin Zhang
- Microbiota I-Center (MagIC)
- Jessie Q. Y. Liang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong
- Yuk Kam Yau
- Microbiota I-Center (MagIC)
- Jiaying Zheng
- Microbiota I-Center (MagIC)
- Chengyu Liu
- Microbiota I-Center (MagIC)
- Mengjing Zhang
- Microbiota I-Center (MagIC)
- Chun Pan Cheung
- Microbiota I-Center (MagIC)
- Jessica Y. L. Ching
- Microbiota I-Center (MagIC)
- Hein M. Tun
- Microbiota I-Center (MagIC)
- Jun Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong
- Francis K. L. Chan
- Microbiota I-Center (MagIC)
- Siew C. Ng
- Microbiota I-Center (MagIC)
- DOI
- https://doi.org/10.1038/s41467-022-34405-3
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 8
Abstract
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.
