生物实体学研究中的大型语言模型:一篇综述
1Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
大型语言模型 (LLM) 为开发生物医学本体学提供了自动化解决方案,改善了生命科学中的知识结构和数据分析. 本综述探讨了LLM的应用,挑战和生物实体学研究的未来趋势.
科学领域:
- 生命科学 生命科学
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 生物医学本体学对于组织生命科学知识和实现综合分析至关重要.
- 传统的本体论开发是耗时的,需要大量的专家投入.
- 人工智能的进步,特别是大型语言模型 (LLM),为简化本体学研究提供了机会.
研究的目的:
- 审查最近关于LLM辅助技术在生物医学本体学发展中的应用研究.
- 综合关于LLM在本体学创建,映射,集成和语义搜索中的发现.
- 识别和讨论基于LLM的生物实体学研究中的挑战和未来方向.
主要方法:
- 最近研究的文献综述,重点关注生物医学本体学的LLM应用.
- 综合与LLM辅助本体创建,映射,集成和语义搜索相关的发现.
- 对挑战的分析,包括偏见,可靠性和道德考虑.
主要成果:
- 在生物医学本体学发展的各个阶段,LLM显示出了自动化和增强生物医学本体学发展的巨大潜力.
- 法学士课程的应用涵盖了本科创建,数据映射,集成和语义搜索功能.
- 关键的挑战包括管理LLM偏见,确保可靠性,并解决道德问题.
结论:
- 士学位准备好改变生物医学本体学的开发,维护和利用.
- 应对挑战对于负责任和有效地将LLMs纳入生物本科学的过程至关重要.
- 未来的研究应该专注于新兴趋势,以进一步推进LLM驱动的生物实体学.
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