Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models (Q30477638)

14 October 2017

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models (English)

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14 October 2017

support vector machine

1 reference

Wen Liu

1

1 reference

14 October 2017

Xiangshan Meng

2

1 reference

14 October 2017

Qiqi Xu

3

1 reference

14 October 2017

Darren R Flower

4

1 reference

14 October 2017

Tongbin Li

5

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14 October 2017

31 March 2006

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14 October 2017

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14 October 2017

7

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14 October 2017

182

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describes a project that uses

14 October 2017

support vector machine

1 reference

based on heuristic

inferred from title

exact match

https://scigraph.springernature.com/pub.10.1186/1471-2105-7-182

0 references

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Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles

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New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

26 November 2018

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26 November 2018

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26 November 2018

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Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction.

26 November 2018

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Application of support vector machines for T-cell epitopes prediction.

26 November 2018

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Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201.

26 November 2018

1 reference

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Prediction of MHC class I binding peptides using profile motifs.

26 November 2018

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Physicochemical explanation of peptide binding to HLA-A*0201 major histocompatibility complex: A three-dimensional quantitative structure-activity relationship study

26 November 2018

1 reference

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Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models.

26 November 2018

1 reference

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Neural network-based prediction of candidate T-cell epitopes.

26 November 2018

1 reference

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Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network.

26 November 2018

1 reference

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Transporter Associated with Antigen Processing Preselection of Peptides Binding to the MHC: A Bioinformatic Evaluation

26 November 2018

1 reference

PubMed

https://pubmed.ncbi.nlm.nih.gov/16579851

inferred from PubMed ID database lookup

12 December 2020

based on heuristic

Toward the Quantitative Prediction of T-Cell Epitopes: CoMFA and CoMSIA Studies of Peptides with Affinity for the Class I MHC Molecule HLA-A*0201

1 reference

PubMed

https://pubmed.ncbi.nlm.nih.gov/16579851

inferred from PubMed ID database lookup

12 December 2020

based on heuristic

Identifiers

DOI

10.1186/1471-2105-7-182

1 reference

14 October 2017

journals/bmcbi/LiuMXFL06

1 reference

DBLP Dataset 2021-01-02

Dimensions Publication ID

28 January 2021

0 references

1 reference

14 October 2017

1 reference