Representation Learning of Human Disease Mechanisms for a Foundation Model in Rare and Common Diseases

This work addresses a critical gap in biomedical AI: developing foundation models capable of representing human disease mechanisms at scale for both rare and common diseases. We leverage large-scale biological knowledge graphs and multi-modal data to learn unified disease representations, enabling zero-shot generalization to unseen conditions. Our approach integrates gene expression, protein interactions, and clinical phenotype data into a joint embedding space, demonstrating strong performance in disease classification, comorbidity prediction, and drug repurposing tasks — with implications for accelerating rare disease research.