ArticleInternational journal of molecular sciences2024
Viral Immunogenicity Prediction by Machine Learning Methods.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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
17 citing papers in PubMed, 24 citations in OpenAlex.
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- 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
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.Immunogenetics · 2026Review
- An integrated computational antigen discovery pipeline with hierarchical filtering for emerging viral variants.NAR molecular medicine · 2026Article
- Predicting genetic evolution of viruses to identify suitable vaccines using artificial intelligence.Scientific reports · 2026Article
- Genome diversity and evolutionary understanding of Lassa virus: a holistic approach highlighting a potential target for cross-lineage vaccine development.Archives of virology · 2025Article
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- Nucleic acid nanobiosystems for cancer theranostics: an overview of emerging trends and challenges.Nanomedicine (London, England) · 2025Review
- Immunoinformatics-driven design of a multi-epitope vaccine against nipah virus: A promising approach for global health protection.Journal, genetic engineering & biotechnology · 2025Article
- Designing a Multi-Epitope Vaccine Against HPV 16, 18, 33, and 45 Targeting L1 and E7 Proteins: An Immunoinformatics Approach for Cervical Cancer Prevention and Therapy.Bioinformatics and biology insights · 2025Article
- Lack of immunogenicity for an influenza-derived peptide across the HLA-B44 supertype molecules.Clinical & translational immunology · 2025Article
- Systems immunology: When systems biology meets immunology.Frontiers in immunology · 2025Review
- Multi-epitope vaccines: a promising strategy against viral diseases in swine.Frontiers in cellular and infection microbiology · 2024Review
- A clinical prediction model for distant metastases of pediatric neuroblastoma: an analysis based on the SEER database.Frontiers in pediatrics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Since viruses are one of the main causes of infectious illnesses, prophylaxis is essential for efficient disease control. Vaccines play a pivotal role in mitigating the transmission of various viral infections and fortifying our defenses against them. The initial step in modern vaccine design and development involves the identification of potential vaccine targets through computational techniques. Here, using datasets of 1588 known viral immunogens and 468 viral non-immunogens, we apply machine learning algorithms to develop models for the prediction of protective immunogens of viral origin. The datasets are split into training and test sets in a 4:1 ratio. The protein structures are encoded by E-descriptors and transformed into uniform vectors by the auto- and cross-covariance methods. The most relevant descriptors are selected by the gain/ratio technique. The models generated by Random Forest, Multilayer Perceptron, and XGBoost algorithms demonstrate superior predictive performance on the test sets, surpassing predictions made by VaxiJen 2.0-an established gold standard in viral immunogenicity prediction. The key attributes determining immunogenicity in viral proteins are specific fingerprints in hydrophobicity and steric properties.
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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.