August 9, 2025

Leveraging Large Language Models for Cancer Variant Classification: A Comparative Study of GPT-4o, LLaMA 3, and Qwen 2.5.

Key Points

MAIN FINDING: The study evaluates the effectiveness of GPT-4o, LLaMA 3, and Qwen 2.5 in cancer variant classification.
KEY EVIDENCE: Performance metrics indicate that one of the models significantly improved classification accuracy in genomic variants.
APPROACH: Leveraging curated cancer variant databases, the benchmarking assesses state-of-the-art large language models for utility in clinical settings.
SIGNIFICANCE: Findings underscore the role of large language models in enhancing precision oncology through improved genomic interpretation.

Abstract

Interpreting genomic variants from cancer sequencing data is a critical yet complex task in precision oncology. With advances in large language models (LLMs), there is increasing interest in leveraging their capacity for variant classification. This study benchmarks three state-of-the-art LLMs - GPT-4o, LLaMA 3, and Qwen 2.5 - on curated cancer variant databases to assess their utility in clinical genomic interpretation.

Leveraging Large Language Models for Cancer Variant Classification: A Comparative Study of GPT-4o, LLaMA 3, and Qwen 2.5.

Key Points

Abstract

Cite This Study