ArticleAnnual review of biomedical data science2020
Immunoinformatics: Predicting Peptide-MHC Binding.
Article in Annual review of biomedical data science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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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.
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Who cites it
59 citing papers in PubMed, 102 citations in OpenAlex.
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- Computational systems immunology and multi-scale modeling for the design of a Multi-Epitope Vaccine (MEV) against emerging multidrug-resistant Klebsiella michiganensis.International microbiology : the official journal of the Spanish Society for Microbiology · 2026Article
- Benchmarking sequence-based and AlphaFold-based methods for pMHC-II binding core prediction: distinct strengths and consensus approaches.BMC medical genomics · 2026Article
- Invas: an inversion-aware method for transcriptome assembly.Nature communications · 2026Article
- Current Melioidosis Diagnostic Landscape and Missed Opportunities in Biomarker Development.Diagnostics (Basel, Switzerland) · 2026Review
- A high-speed attention network for MHC-bound peptide identification and 3D modeling.Cell reports methods · 2026Article
- Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026Review
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- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Quantifying Cross-Attention Interaction in Transformers for Interpreting TCR-pMHC Binding.ArXiv · 2026Article
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- Potent and dose-sparing next-generation SARS-CoV-2 vaccine, mRNA-1283, induces polyfunctional and durable T cell immunity.NPJ vaccines · 2026Article
- Beyond natural amino acids: extending immunogenicity risk assessment to modified peptides through chemical feature encoding. A case study on citrullination.Frontiers in immunology · 2026Article
- STRUMP-I: Structure-Based Machine Learning Approach to pMHC-I Binding Prediction Using Force Field Energy Features.Computational and structural biotechnology journal · 2026Article
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- STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features.bioRxiv : the preprint server for biology · 2025Article
- In Silico Development of a Chimeric Multi-Epitope Vaccine TargetingPharmaceuticals (Basel, Switzerland) · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- The ExoGAN generative AI framework enables extracellular vesicle-based immunotherapy.Research square · 2025Article
- The T cell receptor landscape of childhood brain tumors.Science translational medicine · 2025Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
Abstract
Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.
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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.