用智能体团队从基因本体中挖掘衰老相关知识，提升生物发现效率。
Revisiting Gene Ontology Knowledge Discovery with Hierarchical Feature Selection and Virtual Study Group of AI Agents
- 构建虚拟研究小组，通过分层特征选择聚焦衰老相关基因本体术语。
- 在四种模式生物中验证，多数AI生成结论有文献支持。
- 适合生物信息学与AI融合研究者，推动自动化科学发现。
大型语言模型在多项挑战性任务中表现卓越，其能力可通过新兴的智能体AI技术进一步提升。这一新计算范式正开始革新传统科学发现流程。本文提出一种基于智能体的、面向知识发现的虚拟研究小组，旨在通过分层特征选择方法筛选高度相关的衰老相关基因本体（Gene Ontology）术语，提取有意义的衰老生物学知识。我们通过考察四种不同模式生物的衰老相关基因本体术语，评估所提智能体框架的表现，并通过回顾现有研究文章验证生物学发现。结果表明，大多数AI代理生成的科学主张可被已有文献支持，且所设计的虚拟研究小组内部机制在智能体驱动的知识发现框架中起关键作用。
原文摘要 · Abstract (English)
Large language models have achieved great success in multiple challenging tasks, and their capacity can be further boosted by the emerging agentic AI techniques. This new computing paradigm has already started revolutionising the traditional scientific discovery pipelines. In this work, we propose a novel agentic AI-based knowledge discovery-oriented virtual study group that aims to extract meaningful ageing-related biological knowledge considering highly ageing-related Gene Ontology terms that are selected by hierarchical feature selection methods. We investigate the performance of the proposed agentic AI framework by considering four different model organisms' ageing-related Gene Ontology terms and validate the biological findings by reviewing existing research articles. It is found that the majority of the AI agent-generated scientific claims can be supported by existing literatures and the proposed internal mechanisms of the virtual study group also play an important role in the designed agentic AI-based knowledge discovery framework.
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