Peer-reviewedCC BY-NC-NDThe Lancet Microbe
From microbiomes to predictive ecosystems: challenges and opportunities in artificial intelligence-based approaches
12 affiliations
- 1MOE Key Laboratory of Geriatric Diseases and Immunology, The Second Affiliated Hospital of Soochow University, Soochow University, Suzhou, China
- 2Institute of Clinical Pharmacology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- 3Department of Pediatrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, West China Second University Hospital, Sichuan University, Chengdu, China
- 4National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, School of Medicine, Sun Yat-sen University, Shenzhen, China; Shenzhen Key Laboratory for Systems Medicine in Inflammatory Diseases, Sun Yat-sen University, Shenzhen, China; Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- 5Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- 6Teagasc Food Research Centre, Moorepark, Fermoy, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland
- 7State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- 8Zhejiang Provincial Key Laboratory of Pancreatic Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- 9Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Kidney Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key Laboratory of Organ Transplantation, Shanghai, China; Shanghai Medical College, Fudan University, Shanghai, China
- 10Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China
- 11APC Microbiome Ireland, University College Cork, Cork, Ireland
- 12State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Electronic address: [email protected]
Abstract
Microbiome systems encompass diverse ecological niches and host associations, with their scale and complexity challenging traditional analytical frameworks. Advances in artificial intelligence are transforming microbiome research by enabling improved integrative analyses of microbial genomes, community structure, and functional potential. In this Review, we outline how these developments create opportunities for microbiome research to move beyond descriptive analyses towards predictive modelling and hypothesis generation, including emerging insights into microbial function, host-microbe interactions, and ecosystem-level organisation. As the field grapples with challenges in model interpretability, generalisability, and causal inference, emerging strategies, such as multiomics and multicohort integration, provide promising avenues to deepen biological understanding. Addressing ethical considerations, including data privacy, algorithmic bias, and equitable access, will be crucial for translating artificial intelligence-driven microbiome discoveries into robust and inclusive clinical applications.
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BibTeX
@article{yu2026from,
title = {{From microbiomes to predictive ecosystems: challenges and opportunities in artificial intelligence-based approaches}},
author = {Wanqian Yu and Hua Qu and Shaopu Wang and Mǎng Shī and Yingzhou Lu and Shuting Xia and Catherine Stanton and Chenhong Zhang and Yuhao Wang and Tongyu Zhu and Dong Zhu and Xin Zhou and R Paul Ross and Zhemin Zhou and Yuan Huang},
journal = {The Lancet Microbe},
year = {2026},
month = may,
doi = {10.1016/j.lanmic.2026.101428},
url = {https://doi.org/10.1016/j.lanmic.2026.101428},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - From microbiomes to predictive ecosystems: challenges and opportunities in artificial intelligence-based approaches
AU - Wanqian Yu
AU - Hua Qu
AU - Shaopu Wang
AU - Mǎng Shī
AU - Yingzhou Lu
AU - Shuting Xia
AU - Catherine Stanton
AU - Chenhong Zhang
AU - Yuhao Wang
AU - Tongyu Zhu
AU - Dong Zhu
AU - Xin Zhou
AU - R Paul Ross
AU - Zhemin Zhou
AU - Yuan Huang
PY - 2026
DA - 2026/05/29
JO - The Lancet Microbe
DO - 10.1016/j.lanmic.2026.101428
UR - https://doi.org/10.1016/j.lanmic.2026.101428
AB - Microbiome systems encompass diverse ecological niches and host associations, with their scale and complexity challenging traditional analytical frameworks. Advances in artificial intelligence are transforming microbiome research by enabling improved integrative analyses of microbial genomes, community structure, and functional potential. In this Review, we outline how these developments create opportunities for microbiome research to move beyond descriptive analyses towards predictive modelling and hypothesis generation, including emerging insights into microbial function, host-microbe interactions, and ecosystem-level organisation. As the field grapples with challenges in model interpretability, generalisability, and causal inference, emerging strategies, such as multiomics and multicohort integration, provide promising avenues to deepen biological understanding. Addressing ethical considerations, including data privacy, algorithmic bias, and equitable access, will be crucial for translating artificial intelligence-driven microbiome discoveries into robust and inclusive clinical applications.
SN - 2666-5247
ER -