Biological data
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Using thousands of AI agents to uncover new leads in biological data
research
5 min read
OpenAI, PerturbAI, and Scripps Research used thousands of GPT-Rosalind-powered AI agents to analyze a whole-brain CRISPR atlas containing data from millions of mouse brain cells. The agents examined individual gene disruptions, measured cellular responses, searched scientific literature, and combined their findings into a reasoning atlas linking genes, cell effects, publications, and possible biological mechanisms. The analysis took 32 minutes; the research team estimated comparable sequential human analysis and literature review would take months to a year. The distributed-agent system reproduced findings from human analyses and generated additional research leads. Its novelty and scientific-value assessments aligned with blinded human experts, according to the team. At the largest tested workload, a single agent cited 11 references, while distributed agents cited more than 700, and human experts preferred the distributed interpretations. The agents found that disrupting Tsc2, but not Tsc1, altered dozens of neuronal-communication genes in cerebellar cells, despite both genes regulating mTOR. They also identified a potential link between DCTN1 disruption, increased expression of cholesterol-production genes, and altered cholesterol transport in neurons. DCTN1 mutations cause Perry syndrome, a neurodegenerative disorder with Parkinson-like symptoms. The publicly available reasoning atlas is intended to help researchers prioritize laboratory experiments that can test these proposed mechanisms.