Improving automated deep phenotyping through large language models using retrieval-augmented generation

Improving automated deep phenotyping through large language models using retrieval-augmented generation

Abstract Background Diagnosing rare genetic disorders relies on precise phenotypic and genotypic analysis, with the Human Phenotype Ontology (HPO) providing a standardized language for capturing clinical phenotypes. Rule-based HPO extraction tools use concept recognition to automatically identify ph...

Full description

Saved in:
Bibliographic Details
Main Authors: Brandon T. Garcia, Lauren Westerfield, Priya Yelemali, Nikhita Gogate, E. Andres Rivera-Munoz, Haowei Du, Moez Dawood, Angad Jolly, James R. Lupski, Jennifer E. Posey
Format: Article
Language:English
Published: BMC 2025-08-01
Series:Genome Medicine
Subjects:
Large language models (LLMs)
Retrieval-augmented generation (RAG)
Phenotyping
Human Phenotype Ontology (HPO)
Natural language processing (NLP)
Clinical genomics
Online Access:https://doi.org/10.1186/s13073-025-01521-w
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s13073-025-01521-w

Similar Items