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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: | Printed Book |
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New Jersey
Humana Press
2007
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| Series: | Methods in molecular biology (Clifton, N.J.), v. 409.
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Table of Contents:
- Immunoinformatics and the in silico prediction of immunogenicity. An introduction / D.R. Flower
- Imgt, the international immunogenetics information system for immunoinformatics. Methods for querying imgt databases, tools, and web resources in the context of immunoinformatics / M.P. Lefranc
- The imgt/hla database / J. Robinson and S.G. Marsh
- Ipd: The immuno polymorphism database / J. Robinson and S.G. Marsh
- Syfpeithi: Database for searching and t-cell epitope prediction / M.M. Schuler, M.D. Nastke and S. Stevanovikc
- Searching and mapping of t-cell epitopes, mhc binders, and tap binders / M. Bhasin, S. Lata and G.P. Raghava
- Searching and mapping of b-cell epitopes in bcipep database / S. Saha and G.P. Raghava
- Searching haptens, carrier proteins, and anti-hapten antibodies / S. Srivastava [and others]
- The classification of hla supertypes by grid/cpca and hierarchical clustering methods / P. Guan, I.A. Doytchinova and D.R. Flower
- Structural basis for hla-a2 supertypes / P. Kangueane and M.K. Sakharkar
- Definition of mhc supertypes through clustering of mhc peptide-binding repertoires / P.A. Reche and E.L. Reinherz
- Grouping of class i hla alleles using electrostatic distribution maps of the peptide binding grooves / P. Kangueane and M.K. Sakharkar
- Prediction of peptide-mhc binding using profiles / P.A. Reche and E.L. Reinherz
- Application of machine learning techniques in predicting mhc binders / S. Lata, M. Bhasin and G.P. Raghava
- Artificial intelligence methods for predicting t-cell epitopes / Y. Zhao, M.H. Sung and R. Simon
- Toward the prediction of class i and ii mouse major histocompatibility complex-peptide-binding affinity: In silico bioinformatic step-by-step guide using quantitative structure-activity relationships / C.K. Hattotuwagama, I.A. Doytchinova and D.R. Flower
- Predicting the mhc-peptide affinity using some interactive-type molecular descriptors and qsar models / T.H. Lin
- Implementing the modular mhc model for predicting peptide binding / D.S. DeLuca and R. Blasczyk
- Support vector machine-based prediction of mhc-binding peptides / P. Donnes
- In silico prediction of peptide-mhc binding affinity using svrmhc / W. Liu [and others]
- Hla-peptide binding prediction using structural and modeling principles / P. Kangueane and M.K. Sakharkar
- A practical guide to structure-based prediction of mhc-binding peptides / S. Ranganathan and J.C. Tong
- Static energy analysis of mhc class i and class ii peptide-binding affinity / M.N. Davies and D.R. Flower
- Molecular dynamics simulations: Bring biomolecular structures alive on a computer / S. Wan, P.V. Coveney and D.R. Flower
- An iterative approach to class ii predictions / R.R. Mallios
- Building a meta-predictor for mhc class ii-binding peptides / L. Huang [and others]
- Nonlinear predictive modeling of mhc class ii-peptide binding using bayesian neural networks / D.A. Winkler and F.R. Burden
- Tappred prediction of tap-binding peptides in antigens / M. Bhasin, S. Lata and G.P. Raghava
- Prediction methods for b-cell epitopes / S. Saha and G.P. Raghava
- Histocheck. Evaluating structural and functional mhc similarities / D.S. DeLuca and R. Blasczyk
- Predicting virulence factors of immunological interest / S. Saha and G.P. Raghava
- Immunoinformatics. Predicting immunogenicity in silico. Preface / D.R. Flower.