AI Services Market › Use cases › Security, Science & Hardware
Security, Science & HardwareDone with custom-built or unnamed agent Oct 21, 2026
Mine biological datasets for new leads with thousands of agents
OpenAI describes running thousands of AI agents over complex biological data to uncover new research leads.
- What They Did
- OpenAI, PerturbAI and Scripps Research split analysis of an existing whole-brain CRISPR atlas of mouse brain cells across thousands of GPT-Rosalind agents. Each agent took one gene disruption, analyzed the cellular responses, searched the literature and proposed mechanisms. Their reports were merged into a searchable reasoning atlas. Blinded human experts then judged the outputs.
- How It Went
- The run took 32 minutes, against an estimated months to a year for a person. Experts preferred its reports over a single agent's 75% of the time, and it surfaced leads on Tsc1/Tsc2 and DCTN1-linked cholesterol. These leads still need lab testing.
- Worth Knowing
- A single agent given the same workload cited only 11 references, against more than 700 for the distributed agents, so splitting work per perturbation mattered.
Try it yourself with custom-built or unnamed agent
I have a dataset of [type of biological data, e.g. gene expression CSVs] in [folder path]. Write and run analysis scripts to look for patterns, outliers, or correlations that could be new research leads about [your research question]. Finish with a ranked list of the five most promising leads, each with the evidence behind it, the method used, and caveats, and save the scripts so I can rerun them.