OpenAI agents在32分钟内分析鼠脑数据，这项工作需要人类几个月的时间。
OpenAI与PerturbAI和Scripps Research合作，部署数以千计的AI agents分析数百万老鼠细胞的生物数据。 根据OpenAI的数据，agents运行于GPT-Rosalind上，并通过全脑CRISPR地图集工作，该地图集描绘了基因变化如何影响老鼠脑中不同的细胞类型。
全面分析用了32分钟。 每个agent进行单一基因扰动，分析细胞的反应方式，并搜索科学文献以寻求解释。 该小组估计，进行同样的手工分析需要几个月到一年的时间。
在其他发现中，agents出现了两个与疾病有关的基因之间出乎意料的差异，以及胆固醇调控和神经衰变之间可能存在的联系。 在盲目比较中，人类专家更喜欢分发的agents报告，而不是75%的时间里来自单一的agent的报告。 由此而来的"Reasoning Atlas"可在GitHub上获得，所附的论文在BioRxiv上发布。
OpenAI 称这些线索应有助于研究者决定先在实验室进行测试的机制。
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OpenAI agents analyze mouse brain data in 32 minutes, a job that would take humans months
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.
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