OpenIO is a federated framework integrating high-dimensional immunogenomics with generative AI.
From descriptive immunology to predictive design.
Cancer immunotherapy has reshaped oncology, yet its efficacy is limited by patient heterogeneity and the complex dynamics of the tumor microenvironment (TME). Current reductionist paradigms fail to capture the non-linear context dependence.
OpenIO proposes a paradigm shift: from empirical screening to rational engineering.
The integration of data and models culminates in the Digital Immune Twin. Before a patient is enrolled in a trial, their "Second Me" undergoes high-dimensional simulations.
We are building a suite of AI models trained on the vast datasets within the ImmunoAtlas to learn the "grammar" of immunity.
Trained on millions of receptor sequences to predict antigen specificity, trace clonotypes, and design novel antibodies and TCRs. Moving from reading the repertoire to writing it.
Reconstructing the antigen-presentation pipeline from somatic mutation to MHC binding. Predicting which neoantigens are presented to guide personalized cancer vaccines.
Using Multi-Agent Reinforcement Learning where each cell is an agent. Simulating spatial organization and how barriers like fibrosis affect therapeutic efficacy.
Establishment of ImmunoBank SOPs. Launch of first federated learning nodes. Open source release of immune foundation models.
Release of verified library of AI-designed biologics. First-in-Human study of fully AI-designed biologic in hepatocellular carcinoma.
Self-evolving labs with closed-loop robotic experiments. "Second Me" simulation reports become clinical standard.
We are building the future of immune-oncology together. Integrate your lab into our federated network.
Contribute patient cohorts to ImmunoBank. Validate AI models in prospective clinical trials.
Co-develop multimodal foundation models. Build the "Second Me" digital twin infrastructure.