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008			100301s2007 xxu| s |||| 0|eng d
020			_a9781603271189
024	7		_a10.1007/978-1-60327-118-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aImmunoinformatics _bPredicting Immunogenicity In Silico _cedited by Darren R. Flower.
250			_a1st edition 2007
264		1	_aTotowa, NJ _bHumana Press _c2007
300			_a1 recurso en línea (XV, 438 páginas) _b111 ilustraciones, 5 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v409
505	0		_aDatabases -- 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 -- 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 other Properties of Immune Systems -- TAPPred Prediction of TAP-Binding Peptides in Antigens -- 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.
520			_aImmunoinformatics: 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.
700	1		_aFlower, Darren R _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9781617377259
776	0	8	_iPrinted edition: _z9781588296993
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-60327-118-9 _z(usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
988			_aSpringer_Protocols_2007
999			_c234645 _d234645