GPTON: Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916442469826560 |
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| author | Li, Rongbin Chen, Wenbo Li, Jinbo Xing, Hanwen Xu, Hua Li, Zhao Zheng, W. Jim |
| author_facet | Li, Rongbin Chen, Wenbo Li, Jinbo Xing, Hanwen Xu, Hua Li, Zhao Zheng, W. Jim |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_10899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPTON: Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data Li, Rongbin Chen, Wenbo Li, Jinbo Xing, Hanwen Xu, Hua Li, Zhao Zheng, W. Jim Quantitative Methods Artificial Intelligence J.3; I.2.7 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. |
| title | GPTON: Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data |
| topic | Quantitative Methods Artificial Intelligence J.3; I.2.7 |
| url | https://arxiv.org/abs/2410.10899 |