The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients.

ABSTRACT: Computational methods for identifying gene-disease associations can use both genomic and phenotypic information to prioritize genes and variants that may be associated with genetic diseases. Phenotype-based methods commonly rely on comparing phenotypes observed in a patient with databases of genotype-to-phenotype associations using measures of semantic similarity. They are constrained by the quality and completeness of these resources as well as the quality and completeness of patient phenotype annotation. Genotype-to-phenotype associations used by these methods are largely derived from the literature and coded using phenotype ontologies. Large Language Models (LLMs) have been trained on large amounts of text and data and have shown their potential to answer complex questions across multip

SUBMITTER: Kafkas S 

PROVIDER: S-EPMC12041562 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature