Immunoinformatics Predicting Immunogenicity In Silico
Immunoinformatics: Predicting Immunogenicity In Silico is a primer for researchers interested in this emerging and exciting technology and provides examples in the major areas within the field of immunoinformatics. This volume both engages the reader and provides a sound foundation for the use of im...
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| Format: | eBook |
| Language: | English |
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Totowa, NJ
Humana
2007, 2007
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| Edition: | 1st ed. 2007 |
| Series: | Methods in Molecular Biology
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Databases
- IMGT®, the International ImmunoGeneTics Information System® for Immunoinformatics
- The IMGT/HLA Database
- IPD
- SYFPEITHI
- Searching and Mapping of T-Cell Epitopes, MHC Binders, and TAP Binders
- Searching and Mapping of B-Cell Epitopes in Bcipep Database
- Searching Haptens, Carrier Proteins, and Anti-Hapten Antibodies
- Defining HLA Supertypes
- The Classification of HLA Supertypes by GRID/CPCA and Hierarchical Clustering Methods
- Structural Basis for HLA-A2 Supertypes
- Definition of MHC Supertypes Through Clustering of MHC Peptide-Binding Repertoires
- Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves
- Predicting Peptide-MHC Binding
- Prediction of Peptide-MHC Binding Using Profiles
- Application of Machine Learning Techniques in Predicting MHC Binders
- Artificial Intelligence Methods for Predicting T-Cell Epitopes
- Prediction Methods for B-cell Epitopes
- HistoCheck
- Predicting Virulence Factors of Immunological Interest
- Immunoinformatics and the in Silico Prediction of Immunogenicity
- Immunoinformatics and the in Silico Prediction of Immunogenicity
- Toward the Prediction of Class I and II Mouse Major Histocompatibility Complex-Peptide-Binding Affinity
- Predicting the MHC-Peptide Affinity Using Some Interactive-Type Molecular Descriptors and QSAR Models
- Implementing the Modular MHC Model for Predicting Peptide Binding
- Support Vector Machine-Based Prediction of MHC-Binding Peptides
- In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC
- HLA-Peptide Binding Prediction Using Structural and Modeling Principles
- A Practical Guide to Structure-Based Prediction of MHC-Binding Peptides
- Static Energy Analysis of MHC Class I and Class II Peptide-Binding Affinity
- Molecular Dynamics Simulations
- An Iterative Approach to Class II Predictions
- Building a Meta-Predictor for MHC Class II-Binding Peptides
- Nonlinear Predictive Modeling of MHC Class II-Peptide Binding Using Bayesian Neural Networks
- Predicting otherProperties of Immune Systems
- TAPPred Prediction of TAP-Binding Peptides in Antigens