ArticleVaccines2020
Bacterial Immunogenicity Prediction by Machine Learning Methods.
Article in Vaccines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- In Silico Characterization and Molecular Docking of the L-Arginase Gene From Alcaligenes aquatilis BC2.Biotechnology and applied biochemistry · 2026Article
- High-throughput antigen discovery using Functional Genomic Vaccinology (FGV) identifies protective Streptococcus pneumoniae vaccine candidates.Nature communications · 2026Article
- PU-GRAIL: residue-level graph learning for identifying protective bacterial antigens under positive-unlabeled supervision.Bioinformatics (Oxford, England) · 2026Article
- Integrating dietary patterns and metabolomics with machine learning for metabolic syndrome risk assessment in a rural population.NPJ science of food · 2026Article
- Design of a multi-epitope Staphylococcus aureus vaccine integrating a protein A variant, α-hemolysin, and fibronectin-binding protein A through reverse vaccinology.Scientific reports · 2026Article
- Molecular characterization and immunoinformatics-based design of a multi-epitope vaccine against Staphylococcus nepalensis.Antonie van Leeuwenhoek · 2026Article
- MTB-ImmunogenKG: An LLM-assisted knowledge graph for antigen selection in tuberculosis vaccine research.Biosafety and health · 2026Article
- Immunoinformatics-based design and evaluation of a multi-epitope vaccine against Vibrio fluvialis.Scientific reports · 2026Article
- Immunoinformatics-Based Multi-Epitope Vaccine Targeting Helicobacter Pylori.Cancer reports (Hoboken, N.J.) · 2026Article
- Integrating protein language and geometric deep learning models for enhanced vaccine antigen prediction.Nature communications · 2025Article
- Article
- VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction.Briefings in functional genomics · 2025Article
- Bioinformatic and experimental identification and characterization ofFrontiers in immunology · 2025Article
- SHASI-ML: a machine learning-based approach for immunogenicity prediction inFrontiers in cellular and infection microbiology · 2025Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- Viral Immunogenicity Prediction by Machine Learning Methods.International journal of molecular sciences · 2024Article
- Review
- Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.Journal of chemical theory and computation · 2023Review
- Prediction of Bacterial Immunogenicity by Machine Learning Methods.Methods in molecular biology (Clifton, N.J.) · 2023Article
- COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.Briefings in bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They are able to significantly reduce the experimental work for discovering novel vaccine candidates. Here, we applied six supervised ML methods (partial least squares-based discriminant analysis,
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Identifiers
What OpenQuestion holds
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.