Inferring Drug-Gene Relationships in Cancer Using Literature-Augmented Large Language Models.

ABSTRACT:

Abstract

Understanding drug–gene relationships is essential for advancing targeted cancer therapies and drug repurposing strategies. However, the vast volume of biomedical literature poses significant challenges in efficiently extracting relevant insights. In this study, we developed an automated pipeline that leverages retrieval-augmented large language models (LLM) to infer drug–gene interactions using the most up-to-date biomedical literature. By integrating PubMed and state-of-the-art LLMs, our pipeline generates accurate, evidence-based inferences while addressing the limitations of static LLMs, such as outdated knowledge and the risk of producing misleading results. We systematically validated the pipeline’s performance using curated databases and demonstrated its ability to

SUBMITTER: Lai YJ 

PROVIDER: S-EPMC12036822 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature