用大模型让基因集分析更懂临床，自动筛出真正相关的通路。
Knowledge-guided Contextual Gene Set Analysis Using Large Language Models
- 融合基因聚类、富集分析与大模型，实现上下文感知的通路排序。
- 在19种疾病中提升30%以上性能，专家验证结果更精准可解释。
- 适合癌症研究者快速挖掘疾病特异性生物机制。
基因集分析（GSA）是解读疾病基因组数据的基础方法，通过将基因与生物过程关联来理解疾病机制。但传统GSA方法忽视临床背景，常产生冗长、重复或无关的通路列表，需大量手动筛选，影响结果可靠性与可复现性。为此，我们提出cGSA——一种基于人工智能的新型框架，通过整合基因簇检测、富集分析与大语言模型，实现上下文感知的通路优先排序，识别不仅统计显著且生物学意义明确的通路。在19种疾病和10种疾病相关生物机制上，基于102个手工标注基因集的基准测试显示，cGSA性能优于基线方法超30%；专家验证确认其精度与可解释性显著提升。两项独立病例研究（黑色素瘤与乳腺癌）进一步证明其能发现情境特异性洞见，支持靶向假说生成。
原文摘要 · Abstract (English)
Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility. To address this limitation, we introduce cGSA, a novel AI-driven framework that enhances GSA by incorporating context-aware pathway prioritization. cGSA integrates gene cluster detection, enrichment analysis, and large language models to identify pathways that are not only statistically significant but also biologically meaningful. Benchmarking on 102 manually curated gene sets across 19 diseases and ten disease-related biological mechanisms shows that cGSA outperforms baseline methods by over 30%, with expert validation confirming its increased precision and interpretability. Two independent case studies in melanoma and breast cancer further demonstrate its potential to uncover context-specific insights and support targeted hypothesis generation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。