VF-Fuse: a dual-path feature fusion and iterative update architecture for virulence factor prediction.

ABSTRACT: Accurate prediction of bacterial virulence factors (VFs) is crucial for combating infectious diseases, yet traditional methods often fail to capture their complex sequence properties. We address this challenge by leveraging deep, context-aware representations from large-scale protein language models (PLMs). Our framework begins with a systematic engineering of features from ESM-2 and ProtT5, which confirmed their complementary nature but also revealed that simple concatenation is a suboptimal fusion strategy due to a "feature overshadowing" effect. To overcome this, we developed two novel architectures: VF-Iter, for robust feature enhancement via iterative low-rank updates, and the Dual-Path Feature Fusion (DPF) network, for intelligently integrating the complementary embeddings. The const

SUBMITTER: Huang L 

PROVIDER: S-EPMC12451104 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature