State of the art and challenges in sequence based T-cell epitope prediction
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Structural and Computational Biology in the Design of Immunogenic Vaccine AntigensBioinformatics for cancer immunology and immunotherapyTEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR moleculesWhole-genome immunoinformatic analysis of F. tularensis: predicted CTL epitopes clustered in hotspots are prone to elicit a T-cell response.Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function.Immunity to intracellular Salmonella depends on surface-associated antigens.EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information.Evaluating the immunogenicity of protein drugs by applying in vitro MHC binding data and the immune epitope database and analysis resourcePredictions versus high-throughput experiments in T-cell epitope discovery: competition or synergy?HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicityPrediction of epitopes using neural network based methods.Preclinical models used for immunogenicity prediction of therapeutic proteins.A combined approach of human leukocyte antigen ligandomics and immunogenicity analysis to improve peptide-based cancer immunotherapy.Integrated computational prediction and experimental validation identifies promiscuous T cell epitopes in the proteome of Mycobacterium bovis.Short peptide epitope design from hantaviruses causing HFRS.In silico prediction of tumor antigens derived from functional missense mutations of the cancer gene census.TepiTool: A Pipeline for Computational Prediction of T Cell Epitope Candidates.Bioinformatics identification of antigenic peptide: predicting the specificity of major MHC class I and II pathway players.Computational modelling and inhibitor risk: predicting the future?Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.GradDock: Rapid Simulation and Tailored Ranking Functions for Peptide-MHC Class I Docking.Development of a strategy and computational application to select candidate protein analogues with reduced HLA binding and immunogenicity.
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State of the art and challenges in sequence based T-cell epitope prediction
description
2010 nî lūn-bûn
@nan
2010 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
State of the art and challenges in sequence based T-cell epitope prediction
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State of the art and challenges in sequence based T-cell epitope prediction
@en
State of the art and challenges in sequence based T-cell epitope prediction
@nl
type
label
State of the art and challenges in sequence based T-cell epitope prediction
@ast
State of the art and challenges in sequence based T-cell epitope prediction
@en
State of the art and challenges in sequence based T-cell epitope prediction
@nl
prefLabel
State of the art and challenges in sequence based T-cell epitope prediction
@ast
State of the art and challenges in sequence based T-cell epitope prediction
@en
State of the art and challenges in sequence based T-cell epitope prediction
@nl
P2093
P2860
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P1476
State of the art and challenges in sequence based T-cell epitope prediction
@en
P2093
Claus Lundegaard
Morten Nielsen
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P2888
P356
10.1186/1745-7580-6-S2-S3
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P50
P577
2010-11-03T00:00:00Z
P6179
1036862630