ArticleGenes2024
DTVF: A User-Friendly Tool for Virulence Factor Prediction Based on ProtT5 and Deep Transfer Learning Models.
Article in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- DeepGVS: a bimodal deep learning framework integrating coding-sequence and protein-structural representations for virulence factor prediction.Bioinformatics (Oxford, England) · 2026Article
- Genomic characterization of an unusual bloodstream Weissella viridescens isolate obtained from a Bulgarian intensive care unit patient.Molecular biology reports · 2026Article
- Review
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.Diagnostics (Basel, Switzerland) · 2026Review
- DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics.BMC genomics · 2026Article
- Classification of virulence factors based on dual-channel neural networks with pre-trained language models.PloS one · 2026Article
- DeepVIC: modular prediction and classification of bacterial virulence factors using protein language model embeddings.Bioinformatics advances · 2026Article
- VF-Fuse: a dual-path feature fusion and iterative update architecture for virulence factor prediction.Briefings in bioinformatics · 2025Article
- Generative and Contrastive Self-Supervised Learning for Virulence Factor Identification Based on Protein-Protein Interaction Networks.Microorganisms · 2025Article
- Accurate prediction of virulence factors using pre-train protein language model and ensemble learning.BMC genomics · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Virulencefactors (VFs) are key molecules that enable pathogens to evade the immune systems of the host. These factors are crucial for revealing the pathogenic processes of microbes and drug discovery. Identification of virulence factors in microbes become an important problem in the field of bioinformatics. To address this problem, this study proposes a novel model DTVF (Deep Transfer Learning for Virulence Factor Prediction), which integrates the ProtT5 protein sequence extraction model with a dual-channel deep learning model. In the dual-channel deep learning model, we innovatively integrate long short-term memory (LSTM) with convolutional neural networks (CNNs), creating a novel integrated architecture. Furthermore, by incorporating the attention mechanism, the accuracy of VF detection was significantly enhanced. We evaluated the DTVF model against other excellent-performing models in the field. DTVF demonstrates superior performance, achieving an accuracy rate of 84.55% and an AUROC of 92.08% on the benchmark dataset. DTVF shows state-of-the-art performance in this field, surpassing the existing models in nearly all metrics. To facilitate the use of biologists, we have also developed an interactive web-based user interface version of DTVF based on Gradio.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.