Knowledge-guided Contextual Gene Set Analysis Using Large Language Models

TL;DR

cGSA is an AI-driven framework that improves gene set analysis by incorporating clinical context, leading to more meaningful and interpretable pathway identification in genomic data.

Contribution

The paper introduces cGSA, a novel method that integrates large language models with gene set analysis to incorporate clinical context and enhance interpretability.

Findings

cGSA outperforms baseline methods by over 30% in benchmark tests.

Expert validation confirms increased precision and interpretability.

Case studies demonstrate its ability to uncover context-specific insights.

Abstract

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.