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Advances in Systems Immunology and Machine Learning

The convergence of advanced immune monitoring technologies and artificial intelligence, particularly machine learning, is creating unprecedented opportunities to decode human immunity.  In a comprehensive review published in the Annual Review of Immunology in April 2025, “Systems Human Immunology and AI: Immune Setpoint and Immune Health,” Yona Lei and HIP Co-Chief Science Officer John Tsang review and provide perspectives on how advances in systems immunology and machine learning are being used to map and predict individual immune system states in health and disease. By combining cutting-edge AI with sophisticated immune monitoring, researchers are advancing new concepts about human immunity and developing tools that could transform how we prevent, diagnose, and treat disease.

Recent Posts

  • First Diagnostic Immunome Accurately Predicts Disease

    “Disease diagnostics using machine learning of B cell and T cell receptor sequences”, published in Science on February 21, 2025, is a world-first demonstration of the scientific underpinnings of the Human Immunome Project. The study, led by Maxim Zaslavsky, Erin Craig, Anshul Kundaje, Scott Boyd and colleagues at Stanford and other institutions, introduces a framework called Mal‑ID (MAchine Learning for Immunological Diagnosis).... Read More
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  • Human Immunome Project Congratulates Immunis on XPRIZE Healthspan Award, Announces Leadership Transition

    New York — May 12, 2025 — The Human Immunome Project (HIP) congratulates Dr. Hans Keirstead and Immunis, Inc. for receiving a prestigious XPRIZE Healthspan Semi-finalist award and announces a strategic leadership transition at the conclusion of his two-year term as HIP CEO. Existing HIP Board Chair Jane Metcalfe takes on an Executive Chair role for the next... Read More
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