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利用人工智能代理增强生物医学发现。

Empowering biomedical discovery with AI agents.

机构信息

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA.

出版信息

Cell. 2024 Oct 31;187(22):6125-6151. doi: 10.1016/j.cell.2024.09.022.

Abstract

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.

摘要

我们设想“人工智能科学家”是能够进行批判性学习和推理的系统,通过将人工智能模型和生物医学工具与实验平台集成的协作代理,为生物医学研究提供支持。生物医学人工智能代理不是将人类从发现过程中剔除,而是将人类的创造力和专业知识与人工智能分析大数据集、探索假设空间和执行重复任务的能力结合起来。人工智能代理有望精通各种任务,规划发现工作流程,并进行自我评估,以识别和弥补其知识中的空白。这些代理使用大型语言模型和生成模型为持续学习提供结构化记忆,并使用机器学习工具来整合科学知识、生物原理和理论。人工智能代理可以影响从虚拟细胞模拟、表型可编程控制和细胞电路设计到开发新疗法等各个领域。

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