aceBenchmarking commercial large language models for gene-disease-phenotype extraction from full-text human genetics literature
Manual curation of gene-disease-phenotype relationships from the human genetics literature is a persistent bottleneck for maintaining its bioinformatics databases. Whereas large language models (LLMs) offer a promising alternative, there is currently no systematic benchmark that evaluates whether state-of-the-art commercial LLMs can perform this task reliably on the full-text articles. To address this gap, we introduce a standardized benchmark comprising 406 full-text articles covering 180 congenital heart disea…
Commercial LLMs extracted structured gene, disease, and phenotype fields from full-text genetics papers with high accuracy, which could ease the manual curation bottleneck.
The benchmark covers only congenital heart disease-associated genes, so performance on other disease areas or literature is not established by this source.
Evidence
- Peer-reviewedQuantitative Biology2026-08-17
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Truvace Impact Record TRV-2026-1862, v1: “Benchmarking commercial large language models for gene-disease-phenotype extraction from full-text human genetics literature.” Truvace, 2026-10-18. /record/TRV-2026-1862 (accessed at citation time). sha256 29c2e02e97abe6da…
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