Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease.

ABSTRACT: Phenotype-driven gene prioritization is fundamental to diagnosing rare genetic disorders. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models (LLMs) promise a streamlined text-to-gene solution. In this study, we evaluated five LLMs, including two generative pre-trained transformers (GPT) series and three Llama2 series, assessing their performance across task completeness, gene prediction accuracy, and adherence to required output structures. We conducted experiments, exploring various combinations of models, prompts, phenotypic input types, and task difficulty levels. Our findings revealed that the best-performed LLM, GPT-4, achieved an average accuracy of 17.0% in identifying diagnosed genes within the t

SUBMITTER: Kim J 

PROVIDER: S-EPMC11480789 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature