Reading biology. Building General Medical Intelligence.

Data fragmentation has been the constraint on applying AI to medicine at scale. Through collaborations with leading US academic medical centers, we have connected leading biobanks into a single virtual research network. Common selection criteria, sample quality standards, and data harmonization across sites make it possible to generate AI-ready datasets that combine patient tissue, paired blood samples, longitudinal clinical outcomes, and multimodal molecular data spanning immunology, metabolic, and cardiovascular disease. This is a scale not possible working with any single institutional biobank.

Our AI is designed to learn human biology. We generate multi-modal interpretations of human health across diseases, tissues and molecular modalities. Unlike conventional AI systems, Network Bio has developed an AI infrastructure that is capable of learning biological principles. We overcome the challenges of noisy, multi-site biomedical data through our architecture that understands technical confounders, corrects for batch effects and generates interpretable representations of disease biology.

We imbue our AI into the world’s largest healthcare companies to solve their most challenging problems. We have Fortune-100 partners who use us to interpret novel disease signatures using Network’s AI architecture. Our work with NVIDIA is accelerating the development of biological foundation models.