ReviewTrends in immunology2023
Leveraging deep learning to improve vaccine design.
Review in Trends in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- mRNA lipid nanoparticle vaccines: current status, challenges and future prospects.Molecular biomedicine · 2026Review
- Artificial intelligence in vaccine development: applications, implementation, and future directions.Frontiers in cellular and infection microbiology · 2026Review
- Rational design 2.0: transitioning from static structural biology to computational prioritization and iterative vaccine optimization for RSV.Frontiers in immunology · 2026Review
- Could artificial intelligence gradually replace classical adjuvants?Frontiers in immunology · 2026Review
- Integrating protein language and geometric deep learning models for enhanced vaccine antigen prediction.Nature communications · 2025Article
- Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study.Journal of imaging informatics in medicine · 2025Article
- Deep learning in next-generation vaccine development for infectious diseases.Molecular therapy. Nucleic acids · 2025Review
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
- RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data.Journal of structural biology · 2025Article
- Generative Artificial Intelligence for Virology.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Therapeutic Vaccination in Lung Cancer: Past Attempts, Current Approaches and Future Promises.Journal of respiratory biology and translational medicine · 2025Article
- Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities.Pharmaceutical medicine · 2024Review
- Fostering Tomorrow: Uniting Artificial Intelligence and Social Pediatrics for Comprehensive Child Well-being.Turkish archives of pediatrics · 2024Article
- Review
- Review
- Advances and perspectives in the development of vaccines against highly pathogenic bunyaviruses.Frontiers in cellular and infection microbiology · 2023Review
- Permafrost viremia and immune tweening.Bioinformation · 2023Article
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.
Funding
Abstract
Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning to be realized. Vaccine research and development efforts represent an application with high public health significance. Protein structure prediction, immune repertoire analysis, and phylogenetics are three principal areas in which deep learning is poised to provide key advances. Here, we opine on some of the current challenges with deep learning and how they are being addressed. Despite the nascent stage of deep learning applications in immunological studies, there is ample opportunity to utilize this new technology to address the most challenging and burdensome infectious diseases confronting global populations.
Indexed as
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.