ReviewBMC immunology2018
In silico prediction of cancer immunogens: current state of the art.
Review in BMC immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Designing a Multi-Epitope Vaccine Against NOTCH1 and NOTCH4: A Computational Approach for Triple-Negative Breast Cancer.BioMed research international · 2026Article
- An Integrated Approach to Develop a Potent Vaccine Candidate Construct Against Prostate Cancer by Utilizing Machine Learning and Bioinformatics.Cancer reports (Hoboken, N.J.) · 2024Article
- Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines.International journal of molecular sciences · 2024Review
- The CD4Immunity · 2022Article
- Identification and Design of a Next-Generation Multi Epitopes Bases Peptide Vaccine Candidate Against Prostate Cancer: An In Silico Approach.Cell biochemistry and biophysics · 2020Article
- Computational tools for modern vaccine development.Human vaccines & immunotherapeutics · 2020Review
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
No grant is acknowledged in the PubMed record.
Abstract
Cancer kills 8 million annually worldwide. Although survival rates in prevalent cancers continue to increase, many cancers have no effective treatment, prompting the search for new and improved protocols. Immunotherapy is a new and exciting addition to the anti-cancer arsenal. The successful and accurate identification of aberrant host proteins acting as antigens for vaccination and immunotherapy is a key aspiration for both experimental and computational research. Here we describe key elements of in silico prediction, including databases of cancer antigens and bleeding-edge methodology for their prediction. We also highlight the role dendritic cell vaccines can play and how they can act as delivery mechanisms for epitope ensemble vaccines. Immunoinformatics can help streamline the discovery and utility of Cancer Immunogens.
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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.