A Knowledge-Guided Large Language Model Framework for Microbiome-Based Disease Diagnosis.

Gut microbiome-based disease diagnosis holds significant promise but remains challenging due to the high data dimensionality, typically small sample sizes, and the necessity of incorporating biological knowledge. Due to these challenges, traditional machine learning approaches often tend to overfit the data and fail to capture true biological relationships, resulting in inaccurate diagnoses. To fill in the gap, we propose a two-phase, knowledge-guided large language model (LLM) framework for disease diagnosis that integrates biomedical expertise with in-context learning. In Phase 1, an LLM is employed to identify disease-associated taxa from hundreds of microbial families and to infer their biological relationships with the disease outcome. This process reduces the feature space dimensionality through biologically-informed feature selection and acquires essential domain knowledge. In Phase 2, we employ few-shot prompting to guide the LLM in disease outcome classification based on the domain knowledge acquired in Phase 1. Thanks to the universal applicability of LLM and our two-phase approach, this is a generic framework that can be applied to a wide range of microbiome-based disease diagnostic tasks. We demonstrate the superiority of our framework using inflammatory bowel disease (IBD) as a representative case study, where our approach achieves an accuracy of 73.91%, significantly outperforming an optimized XGBoost classifier. Overall, our knowledge-guided framework provides a powerful and generalizable strategy for leveraging LLMs in microbiome-based disease diagnosis, and opens a new avenue for disease diagnosis in the era of LLM.