Coverart for item
The Resource Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower

Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower

Label
Immunoinformatics : predicting immunogenicity in silico
Title
Immunoinformatics
Title remainder
predicting immunogenicity in silico
Statement of responsibility
edited by Darren R. Flower
Contributor
Subject
Language
eng
Summary
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 immunoinformatics techniques in immunology and vaccinology. The volume is conveniently divided into four sections. The first section, Databases, details various immunoinformatic databases, including IMGT/HLA, IPD, and SYEPEITHI. In the second section, Defining HLA Supertypes, authors discuss supertypes of GRID/CPCA and hierarchical clustering methods, Hla-Ad supertypes, MHC supertypes, and Class I Hla Alleles. The third section, Predicting Peptide-MCH Binding, includes discussions of MCH binders, T-Cell epitopes, Class I and II Mouse Major Histocompatibility, and HLA-peptide binding. Within the fourth section, Predicting Other Properties of Immune Systems, investigators outline TAP binding, B-cell epitopes, MHC similarities, and predicting virulence factors of immunological interest. Immunoinformatics: Predicting Immunogenicity In Silico merges skill sets of the lab-based and the computer-based science professional into one easy-to-use, insightful volume
Member of
Cataloging source
GW5XE
Dewey number
571.960285
Illustrations
illustrations
Index
index present
Language note
English
LC call number
QR182.2.I46
LC item number
I463 2007
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
NLM call number
  • QW 504
  • W1
NLM item number
  • I3247 2007
  • ME9616J v.409 2007
http://library.link/vocab/relatedWorkOrContributorName
Flower, Darren R
Series statement
Methods in molecular biology
Series volume
409
http://library.link/vocab/subjectName
  • Immunoinformatics
  • Immunology
  • Immunological tolerance
  • Computational Biology
  • Immune System
  • Models, Immunological
  • Models, Theoretical
  • Allergy and Immunology
  • Medical Informatics
  • Immunogenetics
  • Databases, Factual
  • Methods
  • Computational Biology
  • Biology
  • Databases as Topic
  • Investigative Techniques
  • Genetics
  • Information Science
  • Medicine
  • Hemic and Immune Systems
  • Models, Biological
  • Informatics
  • Biological Science Disciplines
  • Anatomy
  • Information Storage and Retrieval
  • Health Occupations
  • Natural Science Disciplines
  • SCIENCE
  • Immunological tolerance
  • Allergy and Immunology
  • Computational Biology
  • Medical Informatics
  • Immunogenetics
  • Databases, Factual
  • Immunoinformatics
  • Immunology
  • Immunoinformatics
  • Immunology
Label
Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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
Control code
184907765
Dimensions
unknown
Extent
1 online resource (xv, 438 pages)
Form of item
online
Isbn
9786610945252
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-1-60327-118-9
Other physical details
illustrations (some color).
http://library.link/vocab/ext/overdrive/overdriveId
978-1-58829-699-3
Specific material designation
remote
System control number
(OCoLC)184907765
Label
Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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
Control code
184907765
Dimensions
unknown
Extent
1 online resource (xv, 438 pages)
Form of item
online
Isbn
9786610945252
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-1-60327-118-9
Other physical details
illustrations (some color).
http://library.link/vocab/ext/overdrive/overdriveId
978-1-58829-699-3
Specific material designation
remote
System control number
(OCoLC)184907765

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