Classification of virulence factors based on dual-channel neural networks with pre-trained language models.

ABSTRACT: Virulence factors (VFs) are crucial molecules that enable pathogens to cause infection and disease in a host. They allow pathogens to evade the host's immune defenses and facilitate the progression of infection through various mechanisms. With the increasing prevalence of antibiotic-resistant strains and the emergence of new and re-emerging infectious agents, the classification of VFs has become more critical. This study presents PLM-GNN, an innovative dual-channel model designed for precise classification of VFs, focusing on the seven most numerous types. It integrates a structure channel, which employs a geometric graph neural network to capture the three-dimensional structure features of VFs, and a sequence channel that utilizes a pre-trained language model with Convolutional Neural Net

SUBMITTER: Li G 

PROVIDER: S-EPMC12768247 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature