ArticleCommunications biology2023
CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
- The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.Immunogenetics · 2026Review
- iPepGen: a modular, immunopeptidogenomic analysis pipeline for discovery, verification, and prioritization of cancer peptide neoantigen candidates.Genome biology · 2026Article
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- BeitAI-pHLA: Multiallele Peptide-HLA Class I Binding Prediction Using Protein Language Model and Multi-Instance Learning.Computational and structural biotechnology journal · 2026Article
- TCRLens: structure-aware equivariant graph learning for TCR-pMHC-I recognition and immunogenic epitope discovery.Bioinformatics advances · 2026Article
- High-throughput screening for class I peptide MHC binding via yeast surface display.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Review
- Advances in Therapeutic Cancer Vaccines, Their Obstacles, and Prospects Toward Tumor Immunotherapy.Molecular biotechnology · 2025Review
- VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.Immunogenetics · 2024Article
- Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.Briefings in bioinformatics · 2024Article
- IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction.Briefings in bioinformatics · 2024Article
- ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insights into the results, we explored the essential features that contributed to the prediction. Since the simulation results demonstrated consistency with the experimental studies, we concluded that our method can be utilized for the accurate, rapid, and interpretable peptide-MHC binding prediction to assist biological therapies.
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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.