AI for Microbiome Modeling and Interventions

Program Description

Microbiomes are increasingly recognized as central to human health, environmental sustainability, and industrial biotechnology. Yet, the high-dimensional, multimodal, and context-specific nature of microbiome data presents fundamental challenges for standard AI techniques. AIMMI aims to tackle these challenges by integrating foundation models, causal reasoning, and multi-modal learning to improve prediction, explainability, and generalization across clinical, environmental, and engineered systems. This ambitious task can only be achieved by bringing together academic participants with computer science, microbiology and metabolomics competences. 

The AI for Microbiome Modeling and Interventions (AIMMI) program brings together Denmark’s and European leading researchers in machine learning, microbiome science, and bioengineering to advance the scientific foundations and translational applications of AI in microbial ecosystem modeling. The program is structured around two flagship workshops, each co-led by a major Danish institution: the AI Pioneer Centre and DTU Biosustain, designed to catalyze new collaborations, give an overview of the current state of the field, and seed larger funding efforts. AIMMI collaborates with internationally recognized consortia including COPSAC (clinical microbiome) and CCRP (agricultural microbiome), enabling diversity of impact and applications, while maintaining the common dialog around the shared methods and overlapping AI technologies. 

Methodologically, AIMMI promotes the development of foundation models pretrained on genomic sequences, metagenomic profiles, and mass spectra using self-supervised learning to overcome data sparsity and annotation bottlenecks. These models form the backbone for downstream tasks such as phenotype prediction and compositional shifts. In addition, AIMMI explores multimodal data fusion techniques, such as cross-modal attention, graph neural networks, and contrastive alignment, to link heterogeneous data types like taxonomic profiles, metabolomics, and host clinical features, enabling richer representations of microbial ecosystems and their metabolomics profiles, especially in low-labeled settings where domain adaptation and generalization are critical.