Microbiome based precision medicine through integrated multiomics and machine learning

Abstract

Gut microbiome (GME) is a dynamic ecosystem composed of diverse microorganisms with extensive functional potential that influence host physiology, endocrinology, and neurology. This review explores how multiomics (m^OMICS) and machine learning (ML) enhance understanding of the GME and its implications for human disease and therapy. Integrating metagenomics, metatranscriptomics, metaproteomics, and metabolomics with ML enables the linkage of microbial composition and function to clinical outcomes. Combined m^OMICS approaches elucidate species and strain dynamics, metabolic pathways, and metabolite production within the gut environment. Techniques such as shotgun metagenomics, metagenome-assembled genomes, and pathway mapping reveal associations between dysbiosis and diseases including inflammatory bowel disease, colorectal cancer, cardiometabolic, and neurological disorders. Mechanistic insights highlight short-chain fatty acids in immune regulation, bile acid transformations in metabolic signaling, and trimethylamine N-oxide in cardiovascular risk. ML models trained on heterogeneous datasets identify disease-related microbial modules, improve patient stratification, and predict therapeutic responses, such as differentiating IBD subtypes and detecting cancer-linked microbial signatures. Network analyses uncover gut microbial interaction patterns influencing host physiology. Emerging integrative tools like MOFA+ , DIABLO, and MintTea strengthen cross-modal analysis and biomarker discovery. Standardized workflows addressing quality control, assembly, binning, annotation, and visualization ensure reproducibility. Together, m^OMICS and ML establish a robust framework for translating GME ecology into clinically relevant biomarkers and precision interventions. To enhance reliability, GME studies should adopt uniform sampling protocols, correct compositional biases, employ interpretable models, and validate findings across multi-site cohorts to advance microbiome-based diagnostics and therapeutics in precision medicine.