Enhancing pathogen identification through AI-assisted metagenomic sequencing.

ABSTRACT:

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

SUBMITTER: Peng X 

PROVIDER: S-EPMC12493982 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature