TaxoMFM: A Taxonomy-Aware Foundation Model for the Human Gut Microbiome

Existing gut microbiome foundation models typically use flat taxon representations, limiting their ability to exploit known taxonomic hierarchies. We introduce TaxoMFM, a microbiome foundation model that jointly models four taxonomic ranks spanning species, genus, family, and order. TaxoMFM encodes taxonomic relationships through relation-biased self-attention and performs multiscale compositional reconstruction and hierarchical composition learning with a hierarchical compositional decoder. We further construct TaxoGut-HPP, comprising 100,806 gut microbiome profiles from 231 source cohorts and covering 40 classification and eight regression tasks. TaxoMFM achieves the strongest overall performance across disease, gut health, host trait, and liver ultrasound phenotype prediction. In cross-cohort zero-shot transfer, where target cohorts are excluded from both pretraining and downstream adaptation, TaxoMFM achieves AUROCs of 0.838, 0.736, and 0.722 for colorectal cancer, type 2 diabetes, and inflammatory bowel disease, respectively. Further analyses show that TaxoMFM recovers community changes across taxonomic scales, while preserving community information and producing consistent representations under database updates and species-vocabulary shifts. Our code is available at https://anonymous.4open.science/r/TaxoMFM-F16D/.