The Resource Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower
Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower
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The item Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
- 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
- Language
- eng
- Extent
- 1 online resource (xv, 438 pages)
- 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
- Isbn
- 9786610945252
- Label
- Immunoinformatics : predicting immunogenicity in silico
- Title
- Immunoinformatics
- Title remainder
- predicting immunogenicity in silico
- Statement of responsibility
- edited by Darren R. Flower
- Subject
-
- Allergy and Immunology
- Allergy and Immunology
- Anatomy
- Biological Science Disciplines
- Biology
- Computational Biology
- Computational Biology -- methods
- Computational Biology -- methods
- Databases as Topic
- Databases, Factual
- Databases, Factual
- Genetics
- Health Occupations
- Hemic and Immune Systems
- Immune System
- Immunogenetics
- Immunogenetics -- methods
- Immunoinformatics
- Immunoinformatics
- Immunoinformatics
- Immunological tolerance -- Computer simulation
- Immunological tolerance -- Computer simulation
- Immunology -- Computer simulation
- Immunology -- Computer simulation
- Immunology -- Computer simulation
- Informatics
- Information Science
- Information Storage and Retrieval
- Investigative Techniques
- Medical Informatics
- Medical Informatics -- methods
- Medicine
- Methods
- Models, Biological
- Models, Immunological
- Models, Theoretical
- Natural Science Disciplines
- SCIENCE -- Life Sciences | Anatomy & Physiology
- 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
- 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
- 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
- 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
Subject
- Allergy and Immunology
- Allergy and Immunology
- Anatomy
- Biological Science Disciplines
- Biology
- Computational Biology
- Computational Biology -- methods
- Computational Biology -- methods
- Databases as Topic
- Databases, Factual
- Databases, Factual
- Genetics
- Health Occupations
- Hemic and Immune Systems
- Immune System
- Immunogenetics
- Immunogenetics -- methods
- Immunoinformatics
- Immunoinformatics
- Immunoinformatics
- Immunological tolerance -- Computer simulation
- Immunological tolerance -- Computer simulation
- Immunology -- Computer simulation
- Immunology -- Computer simulation
- Immunology -- Computer simulation
- Informatics
- Information Science
- Information Storage and Retrieval
- Investigative Techniques
- Medical Informatics
- Medical Informatics -- methods
- Medicine
- Methods
- Models, Biological
- Models, Immunological
- Models, Theoretical
- Natural Science Disciplines
- SCIENCE -- Life Sciences | Anatomy & Physiology
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Immunoinformatics--predicting-immunogenicity-in/NcVGWhv7i_g/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Immunoinformatics--predicting-immunogenicity-in/NcVGWhv7i_g/">Immunoinformatics : predicting immunogenicity in silico, edited by Darren R. Flower</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
