From 16S rRNA to deep learning: Evolution of computational approaches in human microbiome studies

The human microbiome plays a vital role in maintaining physiological balance and influencing immunity, metabolism, and disease development. Over the past decade, the field has witnessed a remarkable transition from traditional 16S rRNA sequencing and culture-based assays to advanced computational frameworks that integrate multiomics datasets. Modern computational tools now enable researchers to move beyond descriptive taxonomy toward functional, predictive, and mechanistic insights, with techniques such as network modeling, genome-scale metabolic reconstruction, and deep learning algorithms facilitating the accurate profiling of microbial communities and the identification of biomarkers associated with complex disorders. AI-based platforms including QIIME, MetaPhlAn, and DeepMicro have further strengthened microbiome analysis by integrating genomic, proteomic, and metabolomic data to model host-microbe interactions. Despite these advances, challenges such as data heterogeneity, limited interpretability of AI models, and ethical issues in data sharing continue to impede clinical translation. Future progress will rely on explainable AI, federated learning, and standardized data frameworks to promote transparency, reproducibility, and applicability in precision medicine. Overall, the integration of AI-driven bioinformatics marks a paradigm shift in human microbiome research, transforming it from an observational discipline into a predictive and translational science with the potential to revolutionize personalized healthcare.