Hypophosphatasia x Ehlers Danlos Syndrome
GenAI Rare Disease Hackathon
Research to the People and Stanford Medicine's Rare Disease Hackathon was a catalyst for the first Generative AI models for EDS and Hypophosphatasia. The vision for this event series was to bring AI and medical experts together to build open source language models for rare diseases, and create zero-barrier access to rare disease expertise for patients, researchers and physicians. We are also enthusiastic for the potential AI has to uncover new biomarkers, treatment ideas and novel links between rare diseases. Over the course of 6-months, teams from around the world worked on GenAI models for HPP and EDS. Teams presented their work and gave live demo's at our Demo Day at GitHub in San Francisco. Check out the presentations from Demo Day and deployed models!
Zebra Llama
Zebra Llama is a Generative AI model for Ehlers-Danlos Syndrome. The process of building this model was intended to be instructive for creation of other Rare Disease models.
Rarepath.ai
Frank Hodges, Sundareswar Pullela, Derrick Higgins, Akshay Mulgund, Dr. Stephen Ramsey, Dr. Jared Roach
A free to use AI-driven chat ages designed to assist in the diagnosis, therapeutic target discovery and management of rare diseases. This prototype is geared toward two rare diseases, Hypophosphatasia and Ehlers-Danlos Syndrome.
MIT Team
The MIT team's model focused on Hypophosphatasia from a patient use case perspective.
QIAGEN
Hypophosphatasia: "This biological network describes a complex interplay of various molecules and their effects on bone development, inflammation, and immune responses. Here's a simplified summary:"
Ehlers-Danlos Syndrome: "This biological disease network highlights the complex interplay of genes, proteins, and biological processes involved in the pathogenesis of Ehlers-Danlos syndrome and related conditions."
