ArticlePLoS computational biology2025
Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
- BRIDGE-KO: auditable transfer learning with bridged knockoffs improves cross-cohort microbiome prediction of immunotherapy response.Briefings in bioinformatics · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Maternal and infant gut microbiome.iMeta · 2026Review
- AI-empowered human microbiome research.Gut · 2026Review
- The gut as a central hub for multi-organ crosstalk in aging.Cellular and molecular life sciences : CMLS · 2026Review
- Review
- Transformer Models, Graph Networks, and Generative AI in Gut Microbiome Research: A Narrative Review.Bioengineering (Basel, Switzerland) · 2026Review
- KG-Microbe: Building modular and scalable knowledge graphs for microbiome and microbial sciences.GigaScience · 2026Article
- Review
- Agentic AI for trustworthy synthetic microbial genomics: a perspective on generation, validation, and governance.Frontiers in bioinformatics · 2026Article
- Decoding host-microbiome interactions through integrative multi-omics and artificial intelligence: from biological mechanisms to predictive modelling.Frontiers in microbiomes · 2026Review
- Advances in Molecular Microbiology-From Recent Advances to the Future.Current issues in molecular biology · 2025Article
- Artificial Intelligence-Driven Food Safety: Decoding Gut Microbiota-Mediated Health Effects of Non-Microbial Contaminants.Foods (Basel, Switzerland) · 2025Review
- Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data.PLoS computational biology · 2025Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial taxa and the common compositional patterns in microbial communities. The learned model produces contextualized taxon representations that allow a single microbial taxon to be represented differently according to the specific microbial environment in which it appears. The model further provides a sample representation by collectively interpreting different microbial taxa in the sample and their interactions as a whole. We demonstrate that, while our sample representation performs comparably to baseline models in in-domain prediction tasks such as predicting Irritable Bowel Disease (IBD) and diet patterns, it significantly outperforms them when generalizing to test data from independent studies, even in the presence of substantial distribution shifts. Through a variety of analyses, we further show that the pre-trained, context-sensitive embedding captures meaningful biological information, including taxonomic relationships, correlations with biological pathways, and relevance to IBD expression, despite the model never being explicitly exposed to such signals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.