ReviewNPJ vaccines2024
Development and use of machine learning algorithms in vaccine target selection.
Review in NPJ vaccines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 of them syntheses that pooled it.
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
67 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- An integrated AI-driven vaccine design process: a systematic review of workflows from generative design to translational prediction.Immunologic research · 2026Pooled it
- Pooled it
- Waning immunity and the future of booster vaccination strategies in global vaccine programs post COVID-19.Human vaccines & immunotherapeutics · 2026Review
- DNA-based vaccines: Advances, applications, and future prospects.Genes & diseases · 2026Review
- Emerging and Re-Emerging Viral Infections in Poultry: Integrating Traditional and AI-Based Control Strategies.Current microbiology · 2026Review
- Vaccinology in the twenty-first century revisited.NPJ vaccines · 2026Article
- Integrative reverse vaccinology and computational modeling for the rational design of a broadly immunogenic multi-epitope vaccine against Marburg virus infection.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Identification of Conserved Cross-Reactive B-Cell Epitopes in CPV1 and CPV2 L1 Proteins with Vaccine Potential.Vaccines · 2026Article
- Therapeutic Vaccines for Chronic Viral Infections: From Immune Modulation to Clinical Translation.Vaccines · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- From Sequence to Solution: Computational Design of a Multi-Epitope Vaccine Candidate Against Francisella tularensis.Probiotics and antimicrobial proteins · 2026Article
- Vaccine development againstClinical and experimental vaccine research · 2026Review
- In Silico Design and Characterization of the Essential Outer-Membrane Lipoprotein LolB-Derived Multi-Epitope Vaccine Candidate AgainstMethods and protocols · 2026Article
- Multi-epitope vaccine against nucleoprotein and envelopment polyprotein of Batai orthobunyavirus using molecular docking and molecular dynamics studies.Scientific reports · 2026Article
- The adaptive large language models for vaccine prediction: A novel approach to vaccine demand prediction with engineered deviation prompts.PLOS digital health · 2026Article
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- Review
- Artificial intelligence-guided design of lipid nanoparticles for mRNA delivery.Acta pharmaceutica Sinica. B · 2026Review
- Polymer-lipid hybrid nanoparticle enhances mRNA delivery and T cell-mediated immunity.bioRxiv : the preprint server for biology · 2026Article
7 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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.