GPTON: Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 12 Oct 2024.
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6 of 12 references.
References · 6
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
- 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 182
- 2023Evaluation of large language models for discovery of gene set functioncited by 13
- 2023Gene Set Summarization using Large Language Modelscited by 12
- 2024GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databasescited by 8
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1 paper.