用于知识合成和人工智能增强的生物制造的大型语言模型.
Wenyu Li1, Zhitao Mao2, Zhengyang Xiao3
1Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO 63130, USA; Department of Energy, Environmental, and Chemical Engineering, Washington University in St Louis, St Louis, MO 63130, USA.
Trends in biotechnology
|March 6, 2025
概括
大型语言模型 (LLM) 正在彻底改变合成生物学 (SynBio) 和生物制造. 这些人工智能工具增强了信息提取,知识合成,并为未来的进步自动化实验室流程.
科学领域:
- 合成生物学 合成生物学
- 人工智能的人工智能
- 生物制造 生物制造 生物制造
背景情况:
- 大型语言模型 (LLM) 越来越多地影响科学研究和教育.
- 合成生物学 (SynBio) 是一个快速发展的领域,具有复杂的数据和知识要求.
研究的目的:
- 审查LLMs在合成生物学教育和生物制造方面的进展和潜在影响.
- 为了比较LLM在解决基本SynBio问题的能力.
- 探索LLMs在革新生物制造工艺中的未来作用.
主要方法:
- 关于LLM的最新发展及其在SynBio中的应用的文献综述.
- 对SynBio任务的美国和中国LLM进行比较分析.
- 讨论LLM应用程序,包括信息提取,知识图表构建和检索增强生成.
- 预期LLM集成到设计-构建-测试-学习 (DBTL) 循环和自动驾驶实验室.
主要成果:
- 在从非结构化数据中提取SynBio信息方面,LLM显示出巨大的潜力.
- LLM可以为SynBio.com提供知识图表构建和检索增强生成.
- 士学位准备彻底改变代谢建模,工程,并使生物制造中的自动化实验室成为可能.
结论:
- 法学士课程为合成生物学教育和生物制造提供了变革性的潜力.
- 建立基准,确保可信的知识综合和开发生物安全框架至关重要.
- 人工智能科学家,SynBio研究人员和生物工艺工程师之间的跨学科合作对于实现LLM潜力至关重要.
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