Su, Qi, Liu, Qin, Lau, Raphaela Iris, Zhang, Jingwan, Xu, Zhilu, Yeoh, Yun Kit, Leung, Thomas W.H., Tang, Whitney, Zhang, Lin, Liang, Jessie Q.Y., Yau, Yuk Kam, Zheng, Jiaying, Liu, Chengyu, Zhang, Mengjing, Cheung, Chun Pan, Ching, Jessica Y. L., Tun, Hein M., Yu, Jun, Chan, Francis K.L., and Ng, Siew C. (2022) Faecal microbiome-based machine learning for multi-class disease diagnosis. Nature Communications, 13. 6818.

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Abstract

Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn’s disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91–0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87–0.93) at a specificity of 0.76 to 0.98 (IQR 0.83–0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79–0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.

Item ID:	91190
Item Type:	Article (Research - C1)
ISSN:	2041-1723
Copyright Information:	Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Date Deposited:	14 Apr 2026 00:48
FoR Codes:	32 BIOMEDICAL AND CLINICAL SCIENCES > 3207 Medical microbiology > 320799 Medical microbiology not elsewhere classified @ 100%
SEO Codes:	20 HEALTH > 2001 Clinical health > 200199 Clinical health not elsewhere classified @ 100%
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