ArticleBioinformatics (Oxford, England)2020
MHCAttnNet: predicting MHC-peptide bindings for MHC alleles classes I and II using an attention-based deep neural model.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Toward next-generation therapeutic HPV vaccines: A review on expanding the antigen repertoire and an integrated bioinformatics selection framework.Human vaccines & immunotherapeutics · 2026Review
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Omics-based Cancer Prognosis Across Ethnic Groups: From Feature Engineering to Disparity Detection and Mitigation.IEEE transactions on artificial intelligence · 2026Article
- Identification of MHC Ligands Through Allele-Guided Isolation Combined With Machine Learning for Improved MHC Assignment Using ARDisplay-I.Molecular & cellular proteomics : MCP · 2026Article
- DSCA-HLAII: A dual-stream cross-attention model for predicting peptide-HLA class II interaction and presentation.PLoS computational biology · 2026Article
- Computational design of class II MHC binding peptide with sequence-based evolution information.Bioinformatics advances · 2026Article
- HLAIIPred: cross-attention mechanism for modeling the interaction of HLA class II molecules with peptides.Communications biology · 2025Article
- pMHChat, characterizing the interactions between major histocompatibility complex class II molecules and peptides with large language models and deep hypergraph learning.Briefings in bioinformatics · 2025Article
- Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.Briefings in bioinformatics · 2024Article
- ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.Briefings in bioinformatics · 2024Article
- TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning.Briefings in bioinformatics · 2024Article
- Article
- RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.Bioinformatics (Oxford, England) · 2024Article
- GIHP: Graph convolutional neural network based interpretable pan-specific HLA-peptide binding affinity prediction.Frontiers in genetics · 2024Article
- Review
- GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure.PloS one · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence.Marine drugs · 2023Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationAccurate prediction of binding between a major histocompatibility complex (MHC) allele and a peptide plays a major role in the synthesis of personalized cancer vaccines. The immune system struggles to distinguish between a cancerous and a healthy cell. In a patient suffering from cancer who has a particular MHC allele, only those peptides that bind with the MHC allele with high affinity, help the immune system recognize the cancerous cells.
resultsMHCAttnNet is a deep neural model that uses an attention mechanism to capture the relevant subsequences of the amino acid sequences of peptides and MHC alleles. It then uses this to accurately predict the MHC-peptide binding. MHCAttnNet achieves an AUC-PRC score of 94.18% with 161 class I MHC alleles, which outperforms the state-of-the-art models for this task. MHCAttnNet also achieves a better F1-score in comparison to the state-of-the-art models while covering a larger number of class II MHC alleles. The attention mechanism used by MHCAttnNet provides a heatmap over the amino acids thus indicating the important subsequences present in the amino acid sequence. This approach also allows us to focus on a much smaller number of relevant trigrams corresponding to the amino acid sequence of an MHC allele, from 9251 possible trigrams to about 258. This significantly reduces the number of amino acid subsequences that need to be clinically tested. AVAILABILITY AND IMPLEMENTATION: The data and source code are available at https://github.com/gopuvenkat/MHCAttnNet.
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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.