Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models

Understand

The vast amount of biomedical information available today presents a significant challenge for investigators seeking to digest, process, and understand these findings effectively.

• Large Language Models (LLMs) have emerged as powerful tools to navigate this complex and challenging data landscape.
• However, LLMs may lead to hallucinatory responses, making Retrieval Augmented Generation (RAG) crucial for achieving accurate information.
• In this protocol, we present RUGGED (Retrieval Under Graph-Guided Explainable disease Distinction), a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation, identifying validated paths forward.