Articles liés à Immunoinformatics: A New Technique for MHC Class-II...

Immunoinformatics: A New Technique for MHC Class-II Epitope Prediction - Couverture souple

Omara, Mostafa; Badr, Amr; Nabil, Emad

 
9783659778001: Immunoinformatics: A New Technique for MHC Class-II Epitope Prediction
Couverture souple
ISBN 10 : 3659778001 ISBN 13 : 9783659778001
Editeur : LAP LAMBERT Academic Publishing, 2015

Synopsis

Identification of Major Histocompatibility Complex (MHC) binding peptides is an important step in the selection of T-Cell epitope candidates that are suitable for usage in new vaccines. The binding groove of the MHC Class-II molecule is opened at both sides, which allows for high variability in length of the peptides that bind to this molecule and consequently; complicates the prediction of the binding core motif. An accurate and efficient computational approach for the prediction of such peptides can greatly reduce the time and cost required for the design of new vaccines. EpiGASVM, a new approach for the in silico prediction of MHC Class-II epitopes was developed by combining Genetic Algorithms and Support Vector Machines. Nine variations of EpiGASVM were applied to two sets of similarity-reduced benchmark data. The prediction accuracy and area under the receiver operating characteristic curve were calculated as measures of performance. The technique is compared with some state-of-the-art techniques in this area (e.g. ARB, SMM-Align, PROPRED, NN-Align). Results shows that EpiGASVM is a promising new technique for the solution of the MHC Class-II epitope prediction problem.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Identification of Major Histocompatibility Complex (MHC) binding peptides is an important step in the selection of T-Cell epitope candidates that are suitable for usage in new vaccines. The binding groove of the MHC Class-II molecule is opened at both sides, which allows for high variability in length of the peptides that bind to this molecule and consequently; complicates the prediction of the binding core motif. An accurate and efficient computational approach for the prediction of such peptides can greatly reduce the time and cost required for the design of new vaccines. EpiGASVM, a new approach for the in silico prediction of MHC Class-II epitopes was developed by combining Genetic Algorithms and Support Vector Machines. Nine variations of EpiGASVM were applied to two sets of similarity-reduced benchmark data. The prediction accuracy and area under the receiver operating characteristic curve were calculated as measures of performance. The technique is compared with some state-of-the-art techniques in this area (e.g. ARB, SMM-Align, PROPRED, NN-Align). Results shows that EpiGASVM is a promising new technique for the solution of the MHC Class-II epitope prediction problem.

Biographie de l'auteur

Mostafa Omara is a researcher in Arab Academy for Science and Technology, Cairo. Amr Badr and Emad Nabil work in department on Computer Science, faculty of Computers and Information, Cairo University, Egypt. The author’s research interests include Soft Computing, Machine Learning, P systems and Bioinformatics.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2015
Langue
anglais
ISBN 10 
3659778001
ISBN 13 
9783659778001
Reliure
Broché
Nombre de pages
116
Coordonnées du fabricant
non disponible
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