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MHCBench specifications


Unique identifier OMICS_06694
Name MHCBench
Interface Web user interface
Restrictions to use None
Computer skills Basic
Stability Stable
Maintained Yes

MHCBench citations


An effective and effecient peptide binding prediction approach for a broad set of HLA DR molecules based on ordered weighted averaging of binding pocket profiles

Proteome Sci
PMCID: 3908610
PMID: 24565049
DOI: 10.1186/1477-5956-11-S1-S15

[…] The substitution matrix and the parameters k and θ of the gamma PDF are determined by using the dataset described in [], which contains 14 HLA-DR alleles. The MHCBench, NetMHCIIpan-2.0 HLA-DR ligand and T cell epitope datasets were then used to extensively evaluate the performance of OWA-PSSM through comparing with TEPITOPE, MultiRTA, NetMHCIIpan2.0 and TEP […]


On Evaluating MHC II Binding Peptide Prediction Methods

PLoS One
PMCID: 2533399
PMID: 18813344
DOI: 10.1371/journal.pone.0003268

[…] hold to eliminate any sequence that has a similarity of 80% or greater with one or more sequences in the dataset.In addition, we also used a procedure proposed by Raghava for similarity reduction of MHCBench benchmark datasets. Briefly, given two peptides p1 and p2 of lengths l1 and l2 such that l1≤l2, we compare p1 with each l1-length subpeptide in p2. If the percent identity (PID) between p1 an […]


Predicting Class II MHC Peptide binding: a kernel based approach using similarity scores

BMC Bioinformatics
PMCID: 1664591
PMID: 17105666
DOI: 10.1186/1471-2105-7-501

[…] Multiple data sets were used in the experiments. Eight benchmark data sets with samples of known binders and non-binders of the HLA-DRB1*0401 allele were taken from MHCBench []. Within the data sets, peptide strings are assigned binding strengths of level 0 (non-binders) to 4 (strong binders), and are collected from multiple sources, mainly MHCPEP []. The sets ar […]


Prediction of MHC class II binding peptides based on an iterative learning model

Immunome Res
PMCID: 1325229
PMID: 16351712
DOI: 10.1186/1745-7580-1-6

[…] rly important for the iterative learning procedure as only a small number of nonamers is removed from the positive training set at each iteration.This model was evaluated with benchmark datasets from MHCBench against other major existing methods. The computational study demonstrates overall that this method can achieve comparable or superior performance in comparison with the competing predictors, […]


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