ArticleBiology methods & protocols2025
The effectiveness of large language models with RAG for auto-annotating trait and phenotype descriptions.
Article in Biology methods & protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- OntoCodex: a multi-agent biomedical ontology enrichment framework.npj health systems · 2026Article
- Application of large language models to the annotation of cell lines and mouse strains in genomics data.bioRxiv : the preprint server for biology · 2026Article
- Application of large language models to the annotation of cell lines and mouse strains in genomics data.Database : the journal of biological databases and curation · 2026Article
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
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Abstract
Ontologies are highly prevalent in biology and medicine and are always evolving. Annotating biological text, such as observed phenotype descriptions, with ontology terms is a challenging and tedious task. The process of annotation requires a contextual understanding of the input text and of the ontological terms available. While text-mining tools are available to assist, they are largely based on directly matching words and phrases and so lack understanding of the meaning of the query item and of the ontology term labels. Large Language Models (LLMs), however, excel at tasks that require semantic understanding of input text and therefore may provide an improvement for the auto-annotation of text with ontological terms. Here we describe a series of workflows incorporating OpenAI GPT's capabilities to annotate
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