ArticleFrontiers in digital health2025
A simplified retriever to improve accuracy of phenotype normalizations by large language models.
Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- OntoCodex: a multi-agent biomedical ontology enrichment framework.npj health systems · 2026Article
- Extraction of Human Phenotype Ontology (HPO) Concepts from Clinical Notes Utilizing Large Language Models (LLM) with Model Context Protocol (MCP).medRxiv : the preprint server for health sciences · 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
- From memorization to generalization: fine-tuning large language models for biomedical term-to-identifier normalization.Frontiers in digital health · 2026Article
- Large Language Models in Bio-Ontology Research: A Review.Bioengineering (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Large language models have shown improved accuracy in phenotype term normalization tasks when augmented with retrievers that suggest candidate normalizations based on term definitions. In this work, we introduce a simplified retriever that enhances large language model accuracy by searching the Human Phenotype Ontology (HPO) for candidate matches using contextual word embeddings from BioBERT without the need for explicit term definitions. Testing this method on terms derived from the clinical synopses of Online Mendelian Inheritance in Man (OMIM
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