ArticleBriefings in bioinformatics2022
A comprehensive analysis of the IEDB MHC class-I automated benchmark.
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 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Structure-Guided Immunoinformatics for the Rational Design of a Multi-Epitope Vaccine Against Batai Orthobunyavirus.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Immunoinformatics-driven multi-epitope vaccine design as a promising strategy against multidrug-resistant pathogens: a comprehensive review.Folia microbiologica · 2026Review
- Cancer epitope prediction tools and analysis pipelines in CEDAR.Nucleic acids research · 2026Article
- The Cancer Epitope Database and Analysis Resource (CEDAR): current capabilities and future directions.Frontiers in oncology · 2026Review
- Immunoinformatics Predictions on VariableMicroorganisms · 2025Article
- Immunoinformatics design of an mRNA vaccine against classical swine fever virus using conserved E2 protein and NS3 T-lymphocyte epitopes.BioImpacts : BI · 2025Article
- A modular protein language modelling approach to immunogenicity prediction.PLoS computational biology · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In 2014, the Immune Epitope Database automated benchmark was created to compare the performance of the MHC class I binding predictors. However, this is not a straightforward process due to the different and non-standardized outputs of the methods. Additionally, some methods are more restrictive regarding the HLA alleles and epitope sizes for which they predict binding affinities, while others are more comprehensive. To address how these problems impacted the ranking of the predictors, we developed an approach to assess the reliability of different metrics. We found that using percentile-ranked results improved the stability of the ranks and allowed the predictors to be reliably ranked despite not being evaluated on the same data. We also found that given the rate new data are incorporated into the benchmark, a new method must wait for at least 4 years to be ranked against the pre-existing methods. The best-performing tools with statistically indistinguishable scores in this benchmark were NetMHCcons, NetMHCpan4.0, ANN3.4, NetMHCpan3.0 and NetMHCpan2.8. The results of this study will be used to improve the evaluation and display of benchmark performance. We highly encourage anyone working on MHC binding predictions to participate in this benchmark to get an unbiased evaluation of their predictors.
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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.