Automated benchmarking of peptide-MHC class I binding predictions.
about
Malaria vaccines: identifying Plasmodium falciparum liver-stage targetsHLA class I molecular variation and peptide-binding properties suggest a model of joint divergent asymmetric selection.An open-source computational and data resource to analyze digital maps of immunopeptidomesNNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.Characterization of the peptide binding specificity of the HLA class I alleles B*38:01 and B*39:06.The common equine class I molecule Eqca-1*00101 (ELA-A3.1) is characterized by narrow peptide binding and T cell epitope repertoires.PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinityImmunoinformatics and epitope prediction in the age of genomic medicine.The Length Distribution of Class I-Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele-Specific Binding Preference.NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.TANTIGEN: a comprehensive database of tumor T cell antigens.The role of neoantigens in response to immune checkpoint blockade.Computational genomics tools for dissecting tumour-immune cell interactions.Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction.Genome sequence of Candida versatilis and comparative analysis with other yeast.Analyzing the effect of peptide-HLA-binding ability on the immunogenicity of potential CD8+ and CD4+ T cell epitopes in a large dataset.In silico and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes.Mycobacterium tuberculosis Complex Exhibits Lineage-Specific Variations Affecting Protein Ductility and Epitope Recognition.TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates.Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data.Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.An automated benchmarking platform for MHC class II binding prediction methods.Informatics for cancer immunotherapy.In Silico Prediction of Linear B-Cell Epitopes on Proteins.Amino acid substitutions within HLA-B*27-restricted T cell epitopes prevent recognition by hepatitis delta virus-specific CD8+ T cells.Predicting HLA CD4 Immunogenicity in Human Populations.SNPnexus: assessing the functional relevance of genetic variation to facilitate the promise of precision medicine.Predicting T cell recognition of MHC class I restricted neoepitopesPan-cancer analysis of neoepitopes
P2860
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P2860
Automated benchmarking of peptide-MHC class I binding predictions.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Automated benchmarking of peptide-MHC class I binding predictions.
@ast
Automated benchmarking of peptide-MHC class I binding predictions.
@en
type
label
Automated benchmarking of peptide-MHC class I binding predictions.
@ast
Automated benchmarking of peptide-MHC class I binding predictions.
@en
prefLabel
Automated benchmarking of peptide-MHC class I binding predictions.
@ast
Automated benchmarking of peptide-MHC class I binding predictions.
@en
P2093
P2860
P50
P356
P1433
P1476
Automated benchmarking of peptide-MHC class I binding predictions.
@en
P2093
Imir G Metushi
Jason A Greenbaum
John Sidney
P2860
P304
P356
10.1093/BIOINFORMATICS/BTV123
P407
P577
2015-02-25T00:00:00Z