ArticleBriefings in bioinformatics2022
COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Immune biomarkers, profiles, and responses: a vaccine ontology perspective.Journal of biomedical semantics · 2026Article
- Artificial Intelligence Methods in Infection Biology Research.Methods in molecular biology (Clifton, N.J.) · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Novel SARS-COV2 poly epitope phage-based candidate vaccine and its immunogenicity.Research in pharmaceutical sciences · 2024Article
- Enhancing Vaxign-DL for Vaccine Candidate Prediction with added ESM-Generated Features.bioRxiv : the preprint server for biology · 2024Article
- Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum.NPJ systems biology and applications · 2024Article
- Ontological representation, modeling, and analysis of parasite vaccines.Journal of biomedical semantics · 2024Article
- CanVaxKB: a web-based cancer vaccine knowledgebase.NAR cancer · 2024Article
- Experimental trials of predicted CD4Frontiers in immunology · 2024Article
- Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation.bioRxiv : the preprint server for biology · 2023Article
- The Deceptive COVID-19: Lessons from Common Molecular Diagnostics and a Novel Plan for the Prevention of the Next Pandemic.Diseases (Basel, Switzerland) · 2023Article
- A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.Journal of biomedical semantics · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.
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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.