重新审视基因本体知识发现：基于层次特征选择和人工智能代理虚拟学习小组
Revisiting Gene Ontology Knowledge Discovery with Hierarchical Feature Selection and Virtual Study Group of AI Agents
AI总结：
本文提出基于人工智能代理的虚拟学习小组，通过层次特征选择提取与衰老相关的生物知识，验证了其在不同模式生物中的有效性。
摘要：
大型语言模型在多个具有挑战性的任务中取得了巨大成功，其能力可以通过新兴的代理AI技术进一步提升。这一新的计算范式已经开始革新传统的科学发现流程。在本文中，我们提出了一种新的基于人工智能代理的知识发现导向的虚拟学习小组，旨在提取有意义的与衰老相关的生物知识，考虑通过层次特征选择方法选择的高相关性基因本体术语。我们通过考虑四种不同模式生物的与衰老相关的基因本体术语来研究所提出的人工智能代理框架的性能，并通过审查现有研究文章验证生物学发现。发现大多数人工智能代理生成的科学主张可以由现有文献支持，所提出的虚拟学习小组的内部机制在设计的基于人工智能代理的知识发现框架中也发挥了重要作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
