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

Détails

ID Serval
serval:BIB_2558F57EBAE7
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Application of support vector machines for T-cell epitopes prediction.
Périodique
Bioinformatics
Auteur⸱e⸱s
Zhao Y., Pinilla C., Valmori D., Martin R., Simon R.
ISSN
1367-4803 (Print)
ISSN-L
1367-4803
Statut éditorial
Publié
Date de publication
12/10/2003
Peer-reviewed
Oui
Volume
19
Numéro
15
Pages
1978-1984
Langue
anglais
Notes
Publication types: Comparative Study ; Evaluation Study ; Journal Article ; Validation Study
Publication Status: ppublish
Résumé
The T-cell receptor, a major histocompatibility complex (MHC) molecule, and a bound antigenic peptide, play major roles in the process of antigen-specific T-cell activation. T-cell recognition was long considered exquisitely specific. Recent data also indicate that it is highly flexible, and one receptor may recognize thousands of different peptides. Deciphering the patterns of peptides that elicit a MHC restricted T-cell response is critical for vaccine development.
For the first time we develop a support vector machine (SVM) for T-cell epitope prediction with an MHC type I restricted T-cell clone. Using cross-validation, we demonstrate that SVMs can be trained on relatively small data sets to provide prediction more accurate than those based on previously published methods or on MHC binding.
Data for 203 synthesized peptides is available at http://linus.nci.nih.gov/Data/LAU203_Peptide.pdf
Mots-clé
Algorithms, Antigen-Antibody Complex/chemistry, Antigen-Antibody Complex/metabolism, Artificial Intelligence, Cluster Analysis, Databases, Protein, Epitopes, T-Lymphocyte/chemistry, Epitopes, T-Lymphocyte/metabolism, Histocompatibility Antigens Class I/chemistry, Histocompatibility Antigens Class I/metabolism, Neural Networks, Computer, Protein Binding, Protein Interaction Mapping/methods, Reproducibility of Results, Sensitivity and Specificity, Sequence Analysis, Protein/methods, Structure-Activity Relationship
Pubmed
Web of science
Open Access
Oui
Création de la notice
13/07/2018 11:05
Dernière modification de la notice
18/05/2024 6:58
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