Enhancing pathogen identification through AI-assisted metagenomic sequencing

作者：Xiayu Peng, Yong Wei, Xuedong Zhou · 发表于：Frontiers in Microbiology · 年份：2025 · DOI：10.3389/fmicb.2025.1634194 · 被引用次数：5 · 研究领域：Genomics and Phylogenetic Studies、Gut microbiota and health、Bacterial Identification and Susceptibility Testing

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 ...