AIMC Topic: Major Histocompatibility Complex

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Geometric deep learning improves generalizability of MHC-bound peptide predictions.

Communications biology Dec 19, 2024
The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in autoimmunity, pathogen recognition and tumor immunity. Recent advances in cancer immunotherapies demand for more accurate computational prediction of ...
Protein Binding Computational Biology Major Histocompatibility Complex Humans Peptides Deep Learning
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Integrative analysis of single-cell and bulk RNA sequencing unveils a machine learning-based pan-cancer major histocompatibility complex-related signature for predicting immunotherapy efficacy.

Cancer immunology, immunotherapy : CII May 7, 2024
Major histocompatibility complex (MHC) could serve as a potential biomarker for tumor immunotherapy, however, it is not yet known whether MHC could distinguish potential beneficiaries. Single-cell RNA sequencing datasets derived from patients with im...
Immunotherapy Sequence Analysis, RNA Major Histocompatibility Complex Biomarkers, Tumor Humans Neoplasms Machine Learning Single-Cell Analysis Prognosis
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NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.

Genome biology Sep 6, 2021
Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs usin...
Alleles High-Throughput Nucleotide Sequencing Haplotypes Polymorphism, Single Nucleotide Nanoparticles Benchmarking Chromosome Mapping Major Histocompatibility Complex Nanopore Sequencing Base Sequence Genome, Human INDEL Mutation Humans Neural Networks, Computer
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A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes.

Molecular immunology Aug 26, 2021
The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can on...
Lymphocyte Activation Receptors, Antigen, T-Cell Protein Binding Major Histocompatibility Complex Humans Machine Learning
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A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes.

Nature communications Mar 12, 2021
Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethn...
Histocompatibility Antigens Class II Racial Groups Linkage Disequilibrium Major Histocompatibility Complex Genotype Deep Learning Alleles Diabetes Mellitus, Type 1 Genome-Wide Association Study Humans Histocompatibility Antigens Class I HLA Antigens Genetic Predisposition to Disease Ethnicity
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Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Frontiers in immunology Apr 16, 2019
Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this "convergence" of adaptive immunity among different...
Receptors, Antigen, T-Cell Computer Simulation Computational Biology Adaptive Immunity Humans Datasets as Topic T-Lymphocytes Thermodynamics Epitopes, T-Lymphocyte Machine Learning Major Histocompatibility Complex Antigen Presentation Immunodominant Epitopes
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SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences.

Computational biology and chemistry Oct 27, 2016
At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliab...
Support Vector Machine Major Histocompatibility Complex
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An ontology for major histocompatibility restriction.

Journal of biomedical semantics Jan 11, 2016
BACKGROUND: MHC molecules are a highly diverse family of proteins that play a key role in cellular immune recognition. Over time, different techniques and terminologies have been developed to identify the specific type(s) of MHC molecule involved in ...
Software Animals Databases, Protein Major Histocompatibility Complex Humans Histocompatibility Antigens HLA Antigens Epitopes Gene Ontology
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High-order neural networks and kernel methods for peptide-MHC binding prediction.

Bioinformatics (Oxford, England) Jul 23, 2015
MOTIVATION: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between diff...
Molecular Sequence Data Major Histocompatibility Complex Algorithms Area Under Curve Humans Peptides Protein Binding Epitopes Neural Networks, Computer ROC Curve Support Vector Machine Amino Acid Sequence Databases, Protein
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Multi-positive contrastive learning-based cross-attention model for T cell receptor-antigen binding prediction.

Computer methods and programs in biomedicine Aug 1, 2025
BACKGROUND AND OBJECTIVE: T cells play a vital role in the immune system by recognizing and eliminating infected or cancerous cells, thus driving adaptive immune responses. Their activation is triggered by the binding of T cell receptors (TCRs) to ep...
Humans Machine Learning Algorithms Protein Binding Computational Biology Major Histocompatibility Complex T-Lymphocytes Peptides Receptors, Antigen, T-Cell
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