GPTON：通过本体叙述增强的生成式预训练变换器，用于生物数据的准确注释。
GPTON: Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data.
作者信息
Li Rongbin, Chen Wenbo, Li Jinbo, Xing Hanwen, Xu Hua, Li Zhao, Zheng W Jim
出版信息
ArXiv. 2024 Oct 17:arXiv:2410.10899v2.
Abstract
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.
摘要
通过利用GPT-4进行本体叙述，我们开发了GPTON，通过将本体术语转化为语言，将结构化知识注入到语言模型中，在前五大预测中，超过68%的基因集实现了准确的文本和本体注释。人工评估证实了GPTON的稳健性，突出了其利用语言模型和结构化知识显著推进生物医学研究（超越基因集注释）的潜力。
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