Integrating Complex Network Analysis and Machine Learning for Biomarker Discovery in the Human Gut Microbiome
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Keywords:
Gut microbiome, Complex network analysis, Machine learning, Biomarker discovery, Multi-omics integrationAbstract
The human gut microbiome is a complex ecosystem whose structural and functional
equilibrium is essential for host health. Imbalances in this equilibrium have been linked to numerous
chronic diseases, underscoring the need for sophisticated analytical techniques to elucidate microbiome
composition and predict disease-associated phenotypes. This study proposes an integrated methodology
that combines complicated network spectral analysis with machine learning to discover physiologically
significant patterns from multi-omics microbiome data. Utilizing metagenomic datasets, we calculated
essential measures, including LATENT, EXPLAINED, and MU, which encapsulate the variance structure
and network impact of microbial taxa. Our findings demonstrated a long-tail distribution of LATENT
values, aligning with scale-free network characteristics, suggesting the existence of highly linked taxa that
may function as keystone species or biomarkers. Positive correlations between EXPLAINED and MU
indicate that taxa contributing more to variation also exert a bigger impact within the functional
microbiome network. Statistical distribution analysis, ECDF plots, and comparison boxplots validated a
significant level of variability within the dataset, a characteristic commonly observed in microbial
communities. This integrated framework optimises predictive performance and biological interpretability,
offering a scalable approach for biomarker discovery and the construction of personalised diagnostic
models.
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