Abstract
To enable precise and fully automated cell type annotation with large language models (LLMs), we developed a graph structured feature marker database to retrieve entities linked to differential genes for cell reconstruction. We further designed a multi task workflow to optimize the annotation process. Compared to general purpose LLMs, our method improves human evaluation scores by up to 0.21 and semantic similarity by 6.1% across 11 tissue types, while more closely aligning with the cognitive logic of manual annotation.
Abstract (translated)
为了使用大型语言模型（LLM）实现精确且全自动的细胞类型注释，我们开发了一个基于图结构特征标记数据库，用于检索与差异基因相关联的实体以重建细胞。此外，我们还设计了一种多任务工作流程来优化注释过程。相较于通用型LLM，我们的方法在11种组织类型的评测中提高了人类评估分数高达0.21，并将语义相似性提升了6.1%，同时更紧密地贴合了手动注释的认知逻辑。
URL
https://arxiv.org/abs/2505.00017
