ArticleBriefings in bioinformatics2024
ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 17 citations in OpenAlex.
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- CryoPromptSeg: prompt-guided segmentation with integrated denoising for cryo-EM particle picking.Bioinformatics (Oxford, England) · 2026Article
- Multimodal learning on heterogeneous subgraphs and LLMs representation for MHC-peptide binding affinity prediction.BMC bioinformatics · 2026Article
- IFNIKB: a type I interferon database for antitumuor immunity studies.Database : the journal of biological databases and curation · 2026Article
- Network-informed deconvolution of bulk immune gene co-expression reveals single-cell programs and spatial organization.Frontiers in immunology · 2026Article
- Machine-guided dual-objective protein engineering for deimmunization and therapeutic functions.Cell systems · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- 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
- DGHNN: a deep graph and hypergraph neural network for pan-cancer related gene prediction.Bioinformatics (Oxford, England) · 2025Article
- Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis.BMC medical informatics and decision making · 2025Article
- Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.
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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.