标题： 基于大语言模型的可解释RNA-Seq聚类方法，采用代理证据基础框架 显示英文标题

标题： Interpretable RNA-Seq Clustering with an LLM-Based Agentic Evidence-Grounded Framework

摘要： 我们提出CITE V.1，一种代理的、基于证据的框架，该框架利用大型语言模型（LLMs）提供透明且可重复的RNA-seq聚类解释。与现有基于富集的方法不同，后者将结果简化为广泛统计关联，以及仅使用LLM的模型可能产生无支持的声明或伪造引用，CITE V.1通过生成在生物医学文献中明确锚定的生物学连贯解释来转变聚类解释。该框架协调三个专门的代理：一个检索器从PubMed和UniProt获取领域知识，一个解释器形成功能假设，以及批评者评估声明、强制基于证据的依据，并通过置信度和可靠性指标来限定不确定性。应用于Salmonella enterica RNA-seq数据时，CITE V.1生成了由文献支持的生物学有意义的见解，而仅使用LLM的Gemini基线经常产生带有虚假引用的推测性结果。通过将RNA-seq分析从表面层次的富集转向可审计、可解释和基于证据的假设生成，CITE V.1推动了生物医学中人工智能的透明度和可靠性。 显示英文摘要

摘要： We propose CITE V.1, an agentic, evidence-grounded framework that leverages Large Language Models (LLMs) to provide transparent and reproducible interpretations of RNA-seq clusters. Unlike existing enrichment-based approaches that reduce results to broad statistical associations and LLM-only models that risk unsupported claims or fabricated citations, CITE V.1 transforms cluster interpretation by producing biologically coherent explanations explicitly anchored in the biomedical literature. The framework orchestrates three specialized agents: a Retriever that gathers domain knowledge from PubMed and UniProt, an Interpreter that formulates functional hypotheses, and Critics that evaluate claims, enforce evidence grounding, and qualify uncertainty through confidence and reliability indicators. Applied to Salmonella enterica RNA-seq data, CITE V.1 generated biologically meaningful insights supported by the literature, while an LLM-only Gemini baseline frequently produced speculative results with false citations. By moving RNA-seq analysis from surface-level enrichment to auditable, interpretable, and evidence-based hypothesis generation, CITE V.1 advances the transparency and reliability of AI in biomedicine.