用知识图谱增强大模型，精准标注生物基因数据

GPTON: Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data

• 将知识图谱术语转化为自然语言注入大模型
• 在前五名预测中对68%基因集实现精准标注
• 适合生物信息学与医学研究者使用

通过利用GPT-4进行知识图谱叙述，我们开发了GPTON，将结构化知识以口语化术语注入大模型，实现了对超过68%基因集的文本和知识图谱标注。人工评估验证了GPTON的鲁棒性，表明该方法能有效结合大模型与结构化知识，显著推动生物医学研究，尤其在基因集注释方面具有广阔前景。

原文摘要 · Abstract (English)

By leveraging GPT-4 for ontology narration, we developed GPTON to infuse structured knowledge into LLMs through verbalized ontology terms, achieving accurate text and ontology annotations for over 68% of gene sets in the top five predictions. Manual evaluations confirm GPTON's robustness, highlighting its potential to harness LLMs and structured knowledge to significantly advance biomedical research beyond gene set annotation.

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