HG-LGBM: A Hybrid Model for Microbiome-Disease Prediction Based on Heterogeneous Networks and Gradient Boosting

The microbiome plays a crucial role in maintaining physiological homeostasis and is intricately linked to various diseases. Traditional culture-based microbiological experiments are expensive and time-consuming. Therefore, it is essential to prioritize the development of computational methods that enable further experimental validation of disease-associated microorganisms. Existing computational methods often struggle to effectively capture nonlinear interactions and heterogeneous network structures when predicting microbiome–disease associations. To address this issue, we propose HG-LGBM, an innovative joint prediction framework that combines heterogeneous graph neural networks with a gradient boosting mechanism. We employ a hierarchical heterogeneous graph transformer (HGT) encoder, which utilizes a multi-head attention mechanism to learn higher-order node representations, while LightGBM optimizes the classification task using gradient-boosted decision trees. Evaluated through five-fold cross-validation on the HMDAD and Disbiome datasets, HG-LGBM demonstrated a state-of-the-art performance. The experimental results showed that combining heterogeneous network learning with gradient boosting strategies effectively revealed potential microbiome–disease interactions, providing a powerful tool for biomedical research and precision medicine. Finally, case studies on colorectal cancer and inflammatory bowel disease (IBD) further validated the effectiveness of HG-LGBM.