Download PDFOpen PDF in browserA Transformer Foundation Model for Microbiome Science: Cross-Study Generalization and Automated Discovery4 pages•Published: April 19, 2026AbstractWe present a foundational transformer model for gut microbiome analysis, using self-supervised learning to extract universal principles of microbial community assembly from unlabeled data. Treating microbial communities analogously to languages, our model learns representations enabling reliable cross-study generalization and automated biological discovery. Pretrained on 18,480 samples, our model achieves state-of-the-art performance on multiple downstream tasks, generalizes effectively to independent cohorts with significant distribution shifts, and highlights novel taxa associated with inflammatory bowel disease (IBD). This work exemplifies how foundational AI models can transform scientific domains by learning generalizable patterns that traditional methods miss, opening new avenues for hypothesis generation and understanding in microbiome science.Keyphrases: 16s, diet, electra, foundation model, gut health, inflammatory bowel disease, machine learning, microbiome, out of domain generalization, phylogenetic, self supervised, transformer In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 85-88.
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