Biomedical Hypothesis Explainability with Graph-Based Context Retrieval

Abstract

We introduce an explainability method for biomedical hypothesis generation systems, built on the the novel Hypothesis Generation Context Refiner framework. Our approach combines semantic graph-based retrieval, and relevant data-restrictive training to simulate real-world discovery constraints. Integrated with large language models (LLMs) via retrieval-augmented generation, the system explains hypotheses in contextual evidence using published scientific literature. We propose a novel feedback loop approach, which iteratively identifies and corrects flawed parts of LLM-generated explanations, refining both the evidence paths and supporting papers. We demonstrate the performance of our method with multiple large language models and evaluate explanation and context retrieval quality through both expert-curated assessment and large-scale automated analysis.\\Reproducibility: our code and data are available at [link will be added upon acceptance]