Enhancing pathogen identification through AI-assisted metagenomic sequencing

Introduction: To address the limitations of current metagenomic identification approaches, we proposed a principled AI-assisted architecture that enhances accuracy, scalability, and biological interpretability through three core innovations. Methods: Firstly, we developed a structured probabilistic model that formulates pathogen detection as a hierarchical and compositional inference task under taxonomic and ecological constraints. This framework enables the integration of phylogenetic priors and sparsity-aware mechanisms, reducing noise and ambiguity. By modeling taxonomic structure and ecological dependencies, the approach ensures more accurate identification, especially in complex or low-abundance microbial communities. Secondly, we introduced the Taxon-aware Compositional Inference Network (TCINet), a deep learning model that processes sequencing reads to produce taxonomic embeddings. TCINet estimates abundance distributions via masked neural activations that enforce sparsity and interpretability, while also propagating uncertainty through log-normal variance modeling. Designed to respect microbial phylogeny and co-occurrence patterns, TCINet enables scalable, biologically plausible inference across diverse clinical and environmental datasets. Thirdly, we presented the Hierarchical Taxonomic Reasoning Strategy (HTRS), a post-inference module that refines predictions by enforcing compositional constraints, propagating evidence across taxonomic hierarchies, and calibrating confidence using entropy and variance-based metrics. HTRS includes context-aware thresholding and co-occurrence priors to adaptively optimize performance based on dataset characteristics. Results: Together, these innovations create a unified framework for metagenomic identification that combines probabilistic modeling, deep learning, and structured reasoning. Discussion: The architecture delivers robust and interpretable results, making it suitable for applications in clinical diagnostics, environmental monitoring, and ecological research.