Keyword: Host-Microbiome Interactions

ABSTRACT: The human microbiome functions as a metabolically active organ whose biochemical output is continuously integrated with host physiology. Conventional microbiome surveys, built largely on taxonomic profiling, capture community composition and diversity but resolve neither the functional capacity of these communities nor the bidirectional metabolic exchange that links them to the host. A central limitation is that taxonomy is a poor proxy for function: phylogenetically distinct organisms can perform equivalent reactions, and closely related taxa can diverge metabolically. Resolving host microbiome interactions, therefore, requires integration across heterogeneous, high-dimensional molecular layers, such as metagenomics, metatranscriptomics, proteomics, metabolomics, and host genomic and phenotypic data at a scale and complexity that exceeds classical analytical pipelines. Artificial intelligence (AI) has emerged as a complementary framework for this problem. Machine learning, deep learning, and graph-based models can integrate multi-omics data, infer latent metabolic structure, predict microbial functional potential, and model microbe-metabolite-host relationships as connected networks rather than isolated parts. These approaches have sharpened the discovery of disease-associated microbial and metabolic signatures and candidate therapeutic targets, and they underpin emerging precision medicine applications, including individualized risk stratification, biomarker discovery, and treatment response prediction. Substantial barriers remain, however, including incomplete and non-standardized reference data, limited model interpretability, vulnerability to bias and overfitting, and a shortage of prospective clinical validation. Continued progress in foundation models, real-time microbiome monitoring, and patient-specific metabolic modelling is expected to move the field from descriptive association toward predictive, preventive, and personalized clinical application.