ArticleQuantitative biology (Beijing, China)2026
Benchmarking commercial large language models for gene-disease-phenotype extraction from full-text human genetics literature.
Article in Quantitative biology (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 disease-associated genes, and a multi-dimensional evaluation framework that incorporates fuzzy matching to account for synonyms and partial matches. We benchmarked seven state-of-the-art LLMs, GPT-4o, Claude-Opus-4, DeepSeek-R1, Grok-4, Qwen-3.5, Gemini-2.5 (Pro), and GPT-5 on the extraction of structured gene, disease, and phenotype fields. The top-performing model, Grok-4, achieved 97.6% overall accuracy, whereas the lowest-performing model reached approximately 88%, still surpassing many prior benchmarks employing zero-shot or
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