What question did this study set out to answer?

To evaluate whether large language models can effectively prioritize causative disease-associated genes using clinical phenotype data compared to standard bioinformatics tools.

April 29, 2025Scientific ReportsOpen Access

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

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Authors

ŞKŞenay KafkasKing Abdullah University of Science and Technology MAMarwa AbdelhakimKing Abdullah University of Science and Technology AAAzza AlthagafiTaif University

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Implication

Computational evaluation reveals large language models effectively prioritize causative genes in rare disease cohorts, suggesting AI can streamline genetic diagnostic workflows.

Key Points

To evaluate whether large language models can effectively prioritize causative disease-associated genes using clinical phenotype data compared to standard bioinformatics tools.
Benchmarked large language models against established bioinformatics tools relying on semantic similarity and phenotype ontologies across candidate gene sets ranging from 5 to 100 genes.
Evaluated the diagnostic utility of the approach in a clinical cohort of undiagnosed rare disease patients to identify plausible candidate genes.
Large language models matched or exceeded the performance of dedicated phenotype-similarity bioinformatics tools across candidate set sizes of 5 to 100 genes.
Application to undiagnosed clinical cohorts demonstrated that large language models effectively identify plausible candidate genes to assist in rare disease diagnosis.

Cite This Study

Kafkas et al. (2025) studied this question.

synapsesocial.com/papers/6a154bb1b2e0231f15823dc0 https://doi.org/10.1038/s41598-025-99539-y

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Implication

Computational evaluation reveals large language models effectively prioritize causative genes in rare disease cohorts, suggesting AI can streamline genetic diagnostic workflows.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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