On Evaluating MHC-II Binding Peptide Prediction Methods (Q29036015)

27 March 2017

3

Vasant Honavar

1 reference

27 March 2017

Drena Dobbs

2

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Yasser El-Manzalawy

series ordinal

1

object named as

Yasser EL-Manzalawy

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27 March 2017

language of work or name

English

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publication date

24 September 2008

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27 March 2017

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27 March 2017

volume

3

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27 March 2017

issue

9

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27 March 2017

page(s)

e3268

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Creative Commons Attribution 4.0 International

27 March 2017

copyright license

start time

24 September 2008

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April 2022 Public Data File from Crossref

copyright status

copyrighted

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A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach

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Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

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

3 June 2018

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The three-dimensional structure of peptide-MHC complexes

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21 January 2018

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SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence.

21 January 2018

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A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings

21 January 2018

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A consensus strategy for combining HLA-DR binding algorithms

21 January 2018

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Monosaccharides modulate HCV E2 protein-derived peptide biological properties.

21 January 2018

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Protein homology detection using string alignment kernels.

21 January 2018

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In SilicoTools for Predicting Peptides Binding to HLA-Class II Molecules: More Confusion than Conclusion

21 January 2018

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Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules.

21 January 2018

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Predictive Bayesian neural network models of MHC class II peptide binding.

26 November 2018

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The spectrum kernel: a string kernel for SVM protein classification.

26 November 2018

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10.1371/JOURNAL.PONE.0003268

26 November 2018

Identifiers

DOI

1 reference