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Using thousands of AI agents to uncover new leads in biological data

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Using thousands of AI agents to uncover new leads in biological data

How OpenAI, PerturbAI, and Scripps Research used distributed AI agents to help scientists identify promising research leads in data from millions of brain cells

See how OpenAI, PerturbAI, and Scripps Research deployed thousands of AI agents in parallel to identify promising biological connections in complex data from millions of brain cells.

What if scientists could uncover new clues about Alzheimer’s and Parkinson’s in minutes instead of months? Working together, OpenAI, PerturbAI⁠(opens in a new window), and Scripps Research⁠(opens in a new window) used thousands of AI agents powered by GPT‑Rosalind⁠(opens in a new window) to analyze and interpret existing experimental data from millions of mouse brain cells⁠(opens in a new window) in just 32 minutes. The agents connected the effects of gene disruptions with findings from scientific literature, uncovering unexpected biological connections that can help scientists understand disease and identify research directions for new treatments. The work shows how AI can help researchers explore more of what large biological experiments reveal.

Modern experiments generate far more data than scientists can thoroughly investigate. Researchers can now alter thousands of genes and measure their effects across millions of cells. But understanding what these results mean for human disease requires connecting complex experimental findings with decades of scattered scientific knowledge. This is where AI can help scientists uncover promising leads that might otherwise be missed.

The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning.


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