ArticleiScience2024
HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 12 citations in OpenAlex.
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- The research progress of gastric cancer vaccines: a narrative review.Translational cancer research · 2026Review
- Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026Review
- TCR2HLA: Calibrated inference of HLA genotypes from TCR repertoires enables identification of immunologically relevant metaclonotypes.PLoS computational biology · 2026Article
- Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction.Frontiers in genetics · 2026Review
- Early lymph node T follicular helper cell signalling hub drives influenza vaccine response in an ancestrally diverse cohort.EBioMedicine · 2025Article
- Challenges and opportunities in mRNA vaccine development against bacteria.Nature microbiology · 2025Review
- TCR2HLA: calibrated inference of HLA genotypes from TCR repertoires enables identification of immunologically relevant metaclonotypes.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Peptide-HLA (pHLA) binding prediction is essential in screening peptide candidates for personalized peptide vaccines. Machine learning (ML) pHLA binding prediction tools are trained on vast amounts of data and are effective in screening peptide candidates. Most ML models report the ability to generalize to HLA alleles unseen during training ("pan-allele" models). However, the use of datasets with imbalanced allele content raises concerns about biased model performance. First, we examine the data bias of two ML-based pan-allele pHLA binding predictors. We find that the pHLA datasets overrepresent alleles from geographic populations of high-income countries. Second, we show that the identified data bias is perpetuated within ML models, leading to algorithmic bias and subpar performance for alleles expressed in low-income geographic populations. We draw attention to the potential therapeutic consequences of this bias, and we challenge the use of the term "pan-allele" to describe models trained with currently available public datasets.
Indexed as
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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.