- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
PeptX: Using Genetic Algorithms to optimize peptides for MHC binding ; volume:12 ; number:1 ; day:17 ; month:6 ; year:2011 ; pages:1-9 ; date:12.2011
BMC bioinformatics ; 12, Heft 1 (17.6.2011), 1-9, 12.2011
- Creator
-
Knapp, Bernhard
- Contributor
-
Giczi, Verena
Ribarics, Reiner
Schreiner, Wolfgang
SpringerLink (Online service)
- DOI
-
10.1186/1471-2105-12-241
- URN
-
urn:nbn:de:1111-2016021727264
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:32 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Knapp, Bernhard
- Giczi, Verena
- Ribarics, Reiner
- Schreiner, Wolfgang
- SpringerLink (Online service)
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In silico design of MHC class I high binding affinity peptides through motifs activation map
Prediction of peptides binding to MHC class I and II alleles by temporal motif mining
Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model
Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides
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SVRMHC prediction server for MHC-binding peptides
Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms
Prediction of MHC class I binding peptides, using SVMHC
Prediction of MHC class II binding peptides based on an iterative learning model
In silico design of MHC class I high binding affinity peptides through motifs activation map
Prediction of peptides binding to MHC class I and II alleles by temporal motif mining
Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model
Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides
Identification of allele specific motifs for Major Hisocompatibility Complex (MHC) class II binding peptides using M13 display libraries
ER stress affects processing of MHC class I-associated peptides
Computational immunology : from MHC-peptide binding to immunotherapy
A high-throughput expression screening platform to optimize the production of antimicrobial peptides
SVRMHC prediction server for MHC-binding peptides
Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms
Prediction of MHC class I binding peptides, using SVMHC
Prediction of MHC class II binding peptides based on an iterative learning model
In silico design of MHC class I high binding affinity peptides through motifs activation map
Prediction of peptides binding to MHC class I and II alleles by temporal motif mining
Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model
Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides
Identification of allele specific motifs for Major Hisocompatibility Complex (MHC) class II binding peptides using M13 display libraries
ER stress affects processing of MHC class I-associated peptides
Computational immunology : from MHC-peptide binding to immunotherapy