ArticleBioinformatics advances2026
DeepVIC: modular prediction and classification of bacterial virulence factors using protein language model embeddings.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Motivation: Virulence factors (VFs) play critical roles in bacterial pathogenesis, and identifying these proteins and elucidating their mechanisms is essential for developing effective infection treatments. While protein language models (PLMs) have revolutionized the protein analysis, current Results: To overcome these limitations, we introduce the Deep learning VF Identifier and Classifier (DeepVIC), a modular framework that separates identification and classification into distinct states. The identification module employs a binary classifier using information-rich PLM embeddings. The classification module then integrates these embeddings with evolutionary features derived from position-specific scoring matrices (PSSMs) in a multiclass classifier, structured according to the virulence factor database (VFDB) schema. We benchmarked DeepVIC against six state-of-the-art VF classifiers and predictors using a large independent holdout dataset and two literature-curated datasets for
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