Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications

The human microbiome is a dynamic ecosystem that profoundly influences host physiology through complex molecular interactions. Advances in high-throughput profiling now enable multi-omics measurements at scale, yet integration remains difficult due to biological complexity, technical variability, sparsity, and small cohorts. This review targets bioinformatics practitioners and clinical microbiology researchers applying machine learning to host-microbiome studies. Here, we survey state-of-the-art methods for integrating heterogeneous data types and highlight algorithmic innovations for high dimensionality and small cohorts. We also examine approaches for interpretability that translate mechanistic insight into clinically actionable models. Finally, we outline a standardized benchmarking framework emphasizing open data, rigorous evaluation, and biologically informed architectures. By synthesizing multi-omics measurements with advanced analytics, we chart a pathway toward personalized, microbiome-based therapies while deepening our understanding of host-microbiome crosstalk.