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

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• The human microbiome significantly impacts host health via intricate molecular interactions.
• High-throughput multi-omics data offer insights but face integration challenges (complexity, variability, sparsity, small cohorts).

Purpose of the Study:

• To review state-of-the-art machine learning methods for host-microbiome multi-omics data integration.
• To address challenges in high dimensionality and small sample sizes in these studies.
• To guide bioinformatics practitioners and clinical researchers in applying advanced analytics.

Main Methods:

• Surveying current machine learning techniques for heterogeneous data integration.
• Highlighting algorithmic innovations for high-dimensional and small-cohort data.
• Examining methods for model interpretability to derive clinical insights.

Main Results:

• Identification of advanced analytical approaches for multi-omics host-microbiome data.
• Discussion of strategies for enhancing model interpretability for clinical applications.
• Proposal of a standardized benchmarking framework for rigorous evaluation.

Conclusions:

• Synthesizing multi-omics data with advanced analytics is key to understanding host-microbiome crosstalk.
• This approach paves the way for personalized, microbiome-based therapeutic strategies.
• Standardized frameworks and open data are crucial for reproducible research and clinical translation.