ArticleInternational journal of molecular sciences2017
Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.
Article in International journal of molecular sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
27 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
- VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction.Briefings in functional genomics · 2025Article
- Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.PloS one · 2025Article
- NERVE 2.0: boosting the new enhanced reverse vaccinology environment via artificial intelligence and a user-friendly web interface.BMC bioinformatics · 2024Article
- Review
- Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation.bioRxiv : the preprint server for biology · 2023Article
- Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.Journal of chemical theory and computation · 2023Review
- The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions.Immunoinformatics (Amsterdam, Netherlands) · 2023Review
- Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development.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
- Review
- Vaccine Design by Reverse Vaccinology and Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine.BioMed research international · 2022Review
- Risk factors for carriage of meningococcus in third-level students in Ireland: an unsupervised machine learning approach.Human vaccines & immunotherapeutics · 2021Article
- Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.Nucleic acids research · 2021Article
- An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.Scientific reports · 2021Article
- The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic.Information systems frontiers : a journal of research and innovation · 2021Article
- Article
- Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.The Science of the total environment · 2020Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.
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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.