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.
@article{arxiv.2410.10899,
title = {GPTON: Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data},
author = {Rongbin Li and Wenbo Chen and Jinbo Li and Hanwen Xing and Hua Xu and Zhao Li and W. Jim Zheng},
journal= {arXiv preprint arXiv:2410.10899},
year = {2024}
}