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

arXiv: https://arxiv.org/abs/2410.10899 · HTML: https://mlsanity.com/paper/2410.10899/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval](https://mlsanity.com/paper/2307.00589/) (2023)
- [Evaluation of large language models for discovery of gene set function](https://mlsanity.com/paper/2309.04019/) (2023)
- [Gene Set Summarization using Large Language Models](https://mlsanity.com/paper/2305.13338/) (2023)
- [GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases](https://mlsanity.com/paper/2405.16205/) (2024)

## Built on by (most cited first, 1 of 1)

- [BRAINCELL-AID: An Agentic AI Created Brain Cell Type Resource for Community Annotation](https://mlsanity.com/paper/2510.17064/) (2025)

## 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.