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Items: 1 to 20 of 135

1.

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining.

Meydan C, Otu HH, Sezerman OU.

BMC Bioinformatics. 2013;14 Suppl 2:S13. doi: 10.1186/1471-2105-14-S2-S13. Epub 2013 Jan 21.

2.

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction.

Nielsen M, Lund O.

BMC Bioinformatics. 2009 Sep 18;10:296. doi: 10.1186/1471-2105-10-296.

3.
4.

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms.

Rajapakse M, Schmidt B, Feng L, Brusic V.

BMC Bioinformatics. 2007 Nov 22;8:459.

5.

Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination method.

He J, Yang G, Rao H, Li Z, Ding X, Chen Y.

Artif Intell Med. 2012 Jun;55(2):107-15. doi: 10.1016/j.artmed.2011.10.005. Epub 2011 Nov 30.

PMID:
22134095
6.

Structural properties of MHC class II ligands, implications for the prediction of MHC class II epitopes.

Jørgensen KW, Buus S, Nielsen M.

PLoS One. 2010 Dec 30;5(12):e15877. doi: 10.1371/journal.pone.0015877.

7.
8.

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy.

Kumar N, Mohanty D.

Mol Biosyst. 2010 Dec;6(12):2508-20. doi: 10.1039/c0mb00013b. Epub 2010 Oct 18.

PMID:
20953500
9.
10.

MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes.

Bordner AJ, Mittelmann HD.

BMC Bioinformatics. 2010 Sep 24;11:482. doi: 10.1186/1471-2105-11-482.

11.

Application of machine learning techniques in predicting MHC binders.

Lata S, Bhasin M, Raghava GP.

Methods Mol Biol. 2007;409:201-15. doi: 10.1007/978-1-60327-118-9_14.

PMID:
18450002
12.

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.

Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, Brunak S, Lund O.

Bioinformatics. 2004 Jun 12;20(9):1388-97. Epub 2004 Feb 12.

13.

MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions.

Xu Y, Luo C, Qian M, Huang X, Zhu S.

BMC Genomics. 2014;15 Suppl 9:S9. doi: 10.1186/1471-2164-15-S9-S9. Epub 2014 Dec 8.

14.

Prediction of MHC class I binding peptides, using SVMHC.

Dönnes P, Elofsson A.

BMC Bioinformatics. 2002 Sep 11;3:25.

15.

Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods.

Zhang H, Lundegaard C, Nielsen M.

Bioinformatics. 2009 Jan 1;25(1):83-9. doi: 10.1093/bioinformatics/btn579. Epub 2008 Nov 7.

16.

Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes.

Bordner AJ.

PLoS One. 2010 Dec 20;5(12):e14383. doi: 10.1371/journal.pone.0014383.

17.

NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure.

Nielsen M, Justesen S, Lund O, Lundegaard C, Buus S.

Immunome Res. 2010 Nov 13;6:9. doi: 10.1186/1745-7580-6-9.

18.

Learning a peptide-protein binding affinity predictor with kernel ridge regression.

Giguère S, Marchand M, Laviolette F, Drouin A, Corbeil J.

BMC Bioinformatics. 2013 Mar 5;14:82. doi: 10.1186/1471-2105-14-82.

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20.

Peptide length-based prediction of peptide-MHC class II binding.

Chang ST, Ghosh D, Kirschner DE, Linderman JJ.

Bioinformatics. 2006 Nov 15;22(22):2761-7. Epub 2006 Sep 25.

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