ArticleBriefings in bioinformatics2021
Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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Who cites it
10 citing papers in PubMed.
- Review
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- Recombinant ROP6 protein expressed in Saccharomyces cerevisiae INVSc1 cells induced strong immune response and provided significant protection against toxoplasmosis.Scientific reports · 2025Article
- Article
- Innovative Immunoinformatics Tools for Enhancing MHC (Major Histocompatibility Complex) Class I Epitope Prediction in Immunoproteomics.Protein and peptide letters · 2025Review
- Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201.PloS one · 2025Article
- ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.Briefings in bioinformatics · 2024Article
- A comprehensive analysis of the IEDB MHC class-I automated benchmark.Briefings in bioinformatics · 2022Article
- Prediction of Major Histocompatibility Complex Binding with Bilateral and Variable Long Short Term Memory Networks.Biology · 2022Article
- PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date MHC I binding prediction tools developed over the last decade, thoroughly evaluating feature representation methods, prediction algorithms and model training strategies on a benchmark dataset from Immune Epitope Database. A common limitation was identified during the review that all existing tools can only handle a fixed peptide sequence length. To overcome this limitation, we developed a bilateral and variable long short-term memory (BVLSTM)-based approach, named BVLSTM-MHC. It is the first variable-length MHC class I binding predictor. In comparison to the 10 mainstream prediction tools on an independent validation dataset, BVLSTM-MHC achieved the best performance in six out of eight evaluated metrics. A web server based on the BVLSTM-MHC model was developed to enable accurate and efficient MHC class I binder prediction in human, mouse, macaque and chimpanzee.
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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.