Integrative Mathematical and Machine Learning Approaches for Understanding Gut Microbiome Dynamics and Disease Associations
DOI:
https://doi.org/10.31305/rrijm.2025.v10.n11.037Keywords:
Gut microbiome, mathematical modelling, machine learning, ecological dynamics, disease prediction, longitudinal data, multi omics integration, precision medicineAbstract
The human gut microbiome constitutes a dynamic, complex ecosystem directly linked to host health and disease. Modern high throughput omics technologies generate vast microbial composition and functional data, prompting the need for robust analytical frameworks. Mathematical modeling captures mechanistic interactions and temporal dynamics, while machine learning (ML) excels at high dimensional pattern discovery and disease prediction. Integrative mathematical–ML approaches synergize mechanistic insights with data driven prediction, offering powerful tools to understand microbial community behaviour and its association with disease. This review synthesizes key modeling paradigms ranging from differential equation systems and ecological networks to supervised/unsupervised ML, deep learning and hybrid frameworks. We highlight applications in metabolic disease, inflammatory disorders, cancer immunotherapy, and prognostic disease modeling. Core challenges high dimensionality, compositional data bias, interpretability, and dynamic sampling sparsity are discussed alongside future directions including explainable AI, causal inference, multi omics integration, and personalized predictive modelling. Integrative approaches hold promise for new diagnostics, therapeutic strategies, and precision medicine.
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This is an open access article under the CC BY-NC-ND license Creative Commons Attribution-Noncommercial 4.0 International (CC BY-NC 4.0).
