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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
Published on: August 16, 2017
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Evaluation of large language models for discovery of gene set function
Mengzhou Hu1, Sahar Alkhairy2, Ingoo Lee1
1Department of Medicine, University of California San Diego, La Jolla, California, USA.
Research Square
|October 4, 2023
Summary
Large Language Models (LLMs) like GPT-4 can now assist in functional genomics by generating informative gene set names. This approach offers a more contextual and comprehensive understanding of gene functions compared to traditional methods.
Area of Science:
- Genomics
- Bioinformatics
- Artificial Intelligence
Background:
- Traditional gene set analysis relies on incomplete, manually curated databases.
- Existing methods often lack biological context and struggle with novel discoveries.
Conclusions:
- Large Language Models show promise as functional genomics assistants.
- GPT-4 can rapidly synthesize common gene functions, improving biological context and hypothesis generation.
- LLM-driven approaches offer a valuable complement to existing bioinformatics tools.
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