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

TL;DR

This paper introduces RUGGED, a workflow combining retrieval-augmented generation with explainable graph models to support biomedical hypothesis generation, improve information accuracy, and assist in therapeutic discovery.

Contribution

The paper presents RUGGED, a novel framework integrating retrieval-augmented LLMs with graph-based analysis for explainable biomedical hypothesis generation.

Findings

RUGGED effectively evaluates and recommends therapeutics for cardiomyopathies.

The framework reduces LLM hallucinations and enhances hypothesis exploration.

Demonstrated successful application in clinical case studies.

Abstract

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. Relevant biomedical information from publications and knowledge bases are reviewed, integrated, and extracted via text-mining association analysis and explainable graph prediction models on disease nodes, forecasting potential links among drugs and diseases. These analyses, along with biomedical texts, are integrated into a framework that facilitates user-directed mechanism elucidation as well as hypothesis exploration through RAG-enabled LLMs. A clinical use-case demonstrates RUGGED's ability to evaluate and recommend therapeutics for Arrhythmogenic Cardiomyopathy (ACM) and Dilated Cardiomyopathy (DCM), analyzing prescribed drugs for molecular interactions and unexplored uses. The platform minimizes LLM hallucinations, offers actionable insights, and improves the investigation of novel therapeutics.