Faecal microbiome-based machine learning for multi-class disease diagnosis

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.

Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2022-11-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-022-34405-3

Internet

https://doi.org/10.1038/s41467-022-34405-3

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