Robust and Interpretable Metagenomic Modeling Through Structure-Aware Multi-View Learning and Attribution-Guided Biological Insight.

Integrative modeling of metagenomic and clinical data can advance the study of host phenotypes, but remains challenged by cross-view heterogeneity, uncertain generalizability, and poor interpretability. We developed SAMECAT (Structure-Aware Metagenomics multi-viEw Contrastive AlignmenT), a structure-aware deep learning framework that integrates species-level shotgun metagenomic profiles with mixed-type clinical covariates through view-specific encoders, clustering-informed contrastive alignment, and adaptive representation fusion. Using two independent Louisiana Osteoporosis Study datasets generated through distinct sequencing and bioinformatics pipelines (development n = 1,990; external evaluation n = 481), we evaluated SAMECAT for bone mineral density prediction at four skeletal sites. SAMECAT consistently outperformed single-view models, naive concatenation, alternative deep learning integration approaches, and established machine learning baselines, with performance gains largely preserved in cross-pipeline external evaluation. To improve biological interpretability, we developed a stability-oriented interpretation workflow that aggregates individually low-magnitude and diffusely distributed feature attributions into structured modules, revealing reproducible site-dependent patterns, coherent functional themes, and representative hub taxa. SAMECAT thus provides a robust and interpretable framework for multi-view metagenomic modeling of microbiome-associated host phenotypes.