Microbiome based precision medicine through integrated multiomics and machine learning.

Area of Science:

• Microbiome research and computational biology.

Background:

• The gut microbiome (GME) is a complex ecosystem influencing host health.
• Understanding GME's role in disease requires advanced analytical approaches.

Purpose of the Study:

• To review how multiomics (m^OMICS) and machine learning (ML) advance GME research.
• To highlight the integration of GME data for disease insights and therapeutic development.

Main Methods:

• Integration of metagenomics, metatranscriptomics, metaproteomics, and metabolomics.
• Application of machine learning for analyzing complex GME datasets.
• Utilizing tools like shotgun metagenomics and pathway mapping.

Main Results:

• m^OMICS and ML link GME composition and function to diseases like IBD, cancer, and neurological disorders.
• Identified microbial metabolites (e.g., SCFAs, TMAO) and their physiological roles.
• ML models improve disease stratification and predict treatment responses.

Conclusions:

• m^OMICS and ML provide a framework for translating GME ecology into clinical biomarkers and precision interventions.
• Standardized workflows and multi-site validation are crucial for reliable GME studies.
• This integration advances microbiome-based diagnostics and therapeutics in precision medicine.