ArticleBioinformatics (Oxford, England)2020
Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.Frontiers in immunology · 2020Pooled it
- PU-GRAIL: residue-level graph learning for identifying protective bacterial antigens under positive-unlabeled supervision.Bioinformatics (Oxford, England) · 2026Article
- In silico design and evaluation of a candidate multiepitope Salmonella vaccine construct targeting broad-spectrum protection in poultry.BMC genomics · 2026Article
- Mucosal Vaccine Development: From Adjuvant Design to Next-Generation Delivery Strategies.Biomedicines · 2026Review
- MTB-ImmunogenKG: An LLM-assisted knowledge graph for antigen selection in tuberculosis vaccine research.Biosafety and health · 2026Article
- AntiPan: a genome-informed in silico pipeline for advancing subunit vaccine discovery against Staphylococcus aureus.Scientific reports · 2026Article
- Virulence factors ofFrontiers in microbiology · 2026Article
- A multi-epitope approach for development of a universal vaccine against leptospirosis.Frontiers in immunology · 2026Article
- Integrating protein language and geometric deep learning models for enhanced vaccine antigen prediction.Nature communications · 2025Article
- Computational modelling of a multiepitope vaccine targeting glycoprotein-D for herpes simplex virus 2 (HSV-2): an immunoinformatic analysis.Molecular diversity · 2025Article
- Pathogenicity and virulence ofVirulence · 2025Review
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Artificial Intelligence Driven Framework for the Design and Development of Next-Generation Avian Viral Vaccines.Microorganisms · 2025Review
- Article
- Challenges and opportunities in mRNA vaccine development against bacteria.Nature microbiology · 2025Review
- Spatial transcriptomics identifies novel Pseudomonas aeruginosa virulence factors.Cell genomics · 2025Article
- Article
- MolEpidPred: a novel computational tool for the molecular epidemiology of foot-and-mouth disease virus using VP1 nucleotide sequence data.Briefings in functional genomics · 2025Article
- Artificial Intelligence Methods in Infection Biology Research.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Artificial intelligence and machine learning in the development of vaccines and immunotherapeutics-yesterday, today, and tomorrow.Frontiers in artificial intelligence · 2025Review
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
motivationReverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces challenges in prediction accuracy and program accessibility.
resultsThis study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested 5-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, and one epitope-based method using a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting BPAgs. Vaxign-ML is hosted in a publicly accessible web server and a standalone version is also available. AVAILABILITY AND IMPLEMENTATION: Vaxign-ML website at http://www.violinet.org/vaxign/vaxign-ml, Docker standalone Vaxign-ML available at https://hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https://github.com/VIOLINet/Vaxign-ML-docker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.