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Related Experiment Video

Updated: Jul 6, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Iterative Prompt Refinement for Mining Gene Relationships from ChatGPT.

Yibo Chen1, Jeffrey Gao2, Marius Petruc1

  • 1Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, USA.

Biorxiv : the Preprint Server for Biology
|January 8, 2024
PubMed
Summary
This summary is machine-generated.

ChatGPT can predict gene relationships and biological pathways, but suffers from hallucinations. Refined prompts significantly improve accuracy, enabling complex network construction for disease research.

Keywords:
BioinformaticsChatGPTGene RelationKnowledge GraphPrompt Refinement

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Artificial Intelligence in Genomics

Background:

  • Large language models (LLMs) like ChatGPT show promise for extracting biomedical information.
  • A significant challenge is LLM "hallucination," leading to high false positive rates in extracted data.
  • Accurate gene relationship prediction is crucial for understanding biological pathways and disease mechanisms.

Approach:

  • Systematic evaluation of GPT-3.5-turbo and GPT-4 for predicting gene relationships (activation, inhibition, phosphorylation).
  • Benchmarking against the KEGG Pathway Database and employing diverse prompting strategies.
  • Development and application of an iterative prompt refinement technique using GPT-4 to enhance prompt efficacy.

Key Points:

  • A refined prompt, incorporating a specialized role and explanatory text, significantly improved ChatGPT's performance in predicting gene relationships.
  • The "least-to-most" prompting strategy demonstrated potential for deciphering complex gene interplays and constructing biological networks.
  • Methods were validated using metrics like F-1 score, precision, and recall.

Conclusions:

  • Optimized prompting strategies can mitigate LLM hallucination and enhance the accuracy of gene relationship prediction.
  • LLMs can be leveraged to construct complex gene interaction networks and identify disease-relevant pathways.
  • The developed methods offer a potential framework for various bioinformatics prediction tasks.