OpenAI agents complete mouse brain data analysis in 32 minutes, a task that takes humans months
OpenAI, in collaboration with PerturbAI and Scripps Research, used thousands of AI agents based on GPT-Rosalind to analyze a whole-brain CRISPR atlas covering millions of mouse cells. The entire analysis took 32 minutes, and the team estimates that doing it manually would take months to a year. Each agent was responsible for one gene perturbation, analyzing cellular responses and searching the literature for explanations. The results revealed an unexpected difference between two disease-related genes, as well as a possible link between cholesterol regulation and neurodegeneration. In blind tests, human experts preferred the reports from distributed agents over those from a single agent in 75% of cases; the generated Reasoning Atlas has been made public on GitHub, and the accompanying paper was published on bioRxiv.
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Thousands of agents completed mouse brain data analysis that would originally take months to a year in 32 minutes, and produced a reproducible public atlas and paper.
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OpenAI teamed up with PerturbAI and Scripps Research to deploy thousands of AI agents that analyzed biological data from millions of mouse cells. According to OpenAI , the agents ran on GPT-Rosalind and worked through a whole-brain CRISPR atlas, which maps how gene changes affect different cell types in the mouse brain.
The full analysis took 32 minutes. Each agent took on a single gene perturbation, analyzed how the cells responded, and searched the scientific literature for explanations. The team estimates that doing the same analysis by hand would take anywhere from several months to a year.
Among other findings, the agents turned up unexpected differences between two disease-related genes and a possible link between cholesterol regulation and neurodegeneration. In blind comparisons, human experts preferred the distributed agents' reports over those from a single agent 75 percent of the time. The resulting "Reasoning Atlas" is available on GitHub , and the accompanying paper is out on bioRxiv.
OpenAI says these leads should help researchers decide which mechanisms to test in the lab first.