Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota
Open accessAbstract
The gut microbiome is involved in human health and disease, and its comprehensive understanding is necessary to exploit it as a diagnostic or therapeutic tool. Multi-omics approaches, including metagenomics, metatranscriptomics, metabolomics, and metaproteomics, enable depiction of the gut microbial ecosystem's complexity. However, these tools generate a large data stream in which integration is needed to produce clinically useful readouts, but, in turn, might be difficult to carry out with conventional statistical methods. Artificial intelligence and machine learning have been increasingly applied to multi-omics datasets in several conditions associated with microbiome disruption, from chronic disorders to cancer. Such tools have potential for clinical implementation, including discovery of microbial biomarkers for disease classification or prediction, prediction of response to specific treatments, and fine-tuning of microbiome-modulating therapies. The state of the art, potential, and limits, of artificial intelligence and machine learning in the multi-omics approach to gut microbiome are discussed.
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AI-assisted multi-omics integration has been increasingly applied to investigate how microbiome alterations affect host metabolism and immune signaling in AD.
“AI-assisted multi-omics integration has been increasingly applied to investigate how microbiome alterations affect host metabolism and immune signaling in AD66, 67, 68.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [66]
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- Authors
- Rozera, T., Pasolli, E., Segata, N., & Ianiro, G.
- Affiliations
- Università Cattolica del Sacro Cuore (IT)
- University of Naples Federico II (IT)
- University of Trento (IT)
- Corresponding author
- Ianiro, Gianluca
- Venue
- Gastroenterology
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 169
- Issue
- 3
- Pages
- 487-501
- ISSN
- 0016-5085 (ISSN-L)
- OpenAlex
- W4408612284
- Cited by
- 112
- References
- 21
- Citations by year
- 2026: 83 · 2025: 29
- FWCI
- 9.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Gastroenterology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, cc-by)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion, cc-by)
- Funders
- Associazione Italiana per la Ricerca sul Cancro (30203)
- Ministero della Salute (5\u00D71000, GR\u20132018\u201312365734, PNRR\u2013POC\u20132023\u201312377319)
- National Institutes of Health (1U01CA230551)
- National Cancer Institute
- European Research Council
- Horizon 2020 Framework Programme
- Fondazione Roma
- European Commission
- Ministero dell'Università e della Ricerca (RBFR13EWWI_001)
- Ministero dell’Istruzione, dell’Università e della Ricerca (FIS00001711)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- gut microbiota, multi-omics integration, machine learning, artificial intelligence, metagenomics, metatranscriptomics, metabolomics, metaproteomics, microbial biomarkers, disease classification, treatment response prediction, microbiome disruption
- MeSH terms
- Gastrointestinal Microbiome, Machine Learning, Multiomics, Artificial Intelligence, Humans, Genomics, Proteomics, Metabolomics, Metagenomics
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