Huang Di, Li Hao, Li Wenyu, Zhang Heming, Dickson Patricia, Zhan Ming, Miller J Philip, Cruchaga Carlos, Province Michael, Chen Yixin, Payne Philip, Li Fuhai
Institute for Informatics (I2).
Computer Science & Engineering.
bioRxiv. 2025 Aug 2:2025.07.31.667797. doi: 10.1101/2025.07.31.667797.
The convergence of large language models (LLMs), AIagents, and large-scale omic datasets-such as single-cell omics, marks the arrival of a critical inflection point in biomedical research, via autonomous data mining and novel hypothesis generation. However, there is no specifically designed agentic AI model that can systematically integrate large-scale single-cell (sc) RNAseq (covering diverse diseases and cell types), omic data analytic tools, accumulated biomedical knowledge, and literature search to facilitate autonomous scientific discovery in precision medicine. In this study, we develop a novel agentic AI, OmniCellAgent, to empower non-computational-expert users-such as patients and family members, clinicians, and wet-lab researchers-to conduct scRNA-seq data-driven biomedical research like experts, uncovering molecular disease mechanisms and identifying effective precision therapies. The code of omniCellAgent is publicly accessible at: https://fuhailiailab.github.io/.
大语言模型（LLMs）、人工智能代理和大规模组学数据集（如单细胞组学）的融合，通过自主数据挖掘和新假设生成，标志着生物医学研究中一个关键转折点的到来。然而，目前还没有专门设计的智能人工智能模型能够系统地整合大规模单细胞（sc）RNA测序（涵盖多种疾病和细胞类型）、组学数据分析工具、积累的生物医学知识以及文献检索，以促进精准医学中的自主科学发现。在本研究中，我们开发了一种新型智能人工智能OmniCellAgent，使非计算专家用户（如患者及其家属、临床医生和湿实验室研究人员）能够像专家一样进行基于scRNA测序数据的生物医学研究，揭示分子疾病机制并确定有效的精准治疗方法。OmniCellAgent的代码可在以下网址公开获取：https://fuhailiailab.github.io/ 。
