Machine learning driven multi omics and probiotic integration for precision colorectal cancer management

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, driven by tumor heterogeneity, late-stage detection, and variable therapeutic response. The integration of machine learning (ML) with multi-omics and probiotic research offers a transformative pathway toward precision CRC management. ML enables high-dimensional analysis of genomic, proteomic, metabolomic, and microbiome data, facilitating biomarker discovery, early disease detection, and individualized therapeutic design. Supervised and deep learning models have identified key microbial taxa, including Fusobacterium, Parvimonas, and Peptostreptococcus, as potential diagnostic biomarkers, while explainable AI enhances interpretability and clinical trust. Multi-omics integration bridges microbial and host metabolic interactions, revealing mechanisms linking dysbiosis with tumorigenesis. In parallel, ML-driven frameworks accelerate probiotic discovery, safety profiling, and the design of next-generation strains with anti-tumor and immunomodulatory properties. Reinforcement and adaptive learning models further enable personalized probiotic interventions by simulating host–microbiome dynamics. Despite challenges related to data heterogeneity, algorithmic bias, and regulatory validation, the convergence of ML, synthetic biology, and explainable AI heralds a new era of precision oncology. This integrative paradigm has the potential to transform probiotics from empirical supplements into intelligent, personalized therapeutics. Future directions emphasize the need for multi-cohort validation, robust model development to minimize overfitting, and the integration of ethical and regulatory frameworks to ensure reliable and clinically translatable ML applications in CRC management.