Preface |
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ix | |
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1 Immune Systems and Systems Biology |
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1 | (16) |
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1.1 Innate and Adaptive Immunity in Vertebrates |
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10 | (1) |
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1.2 Antigen Processing and Presentation |
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11 | (3) |
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1.3 Individualized Immune Reactivity |
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14 | (3) |
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2 Contemporary Challenges to the Immune System |
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17 | (18) |
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2.1 Infectious Diseases in the New Millennium |
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17 | (1) |
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2.2 Major Killers in the World |
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17 | (4) |
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21 | (1) |
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2.4 Clustering of Infectious Disease Organisms |
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22 | (2) |
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24 | (6) |
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30 | (1) |
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31 | (1) |
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32 | (3) |
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3 Sequence Analysis in Immunology |
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35 | (34) |
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35 | (1) |
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36 | (16) |
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52 | (2) |
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54 | (1) |
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3.5 Molecular Evolution and Phylogeny |
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55 | (2) |
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3.6 Viral Evolution and Escape: Sequence Variation |
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57 | (4) |
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3.7 Prediction of Functional Features of Biological Sequences |
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61 | (8) |
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4 Methods Applied in Immunological Bioinformatics |
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69 | (34) |
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4.1 Simple Motifs, Motifs and Matrices |
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69 | (3) |
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4.2 Information Carried by Immunogenic Sequences |
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72 | (3) |
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4.3 Sequence Weighting Methods |
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75 | (2) |
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4.4 Pseudocount Correction Methods |
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77 | (2) |
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4.5 Weight on Pseudocount Correction |
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79 | (1) |
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4.6 Position Specific Weighting |
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79 | (1) |
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80 | (4) |
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84 | (7) |
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4.9 Artificial Neural Networks |
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91 | (8) |
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4.10 Performance Measures for Prediction Methods |
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99 | (3) |
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4.11 Clustering and Generation of Representative Sets |
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102 | (1) |
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5 DNA Microarrays in Immunology |
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103 | (8) |
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5.1 DNA Microarray Analysis |
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103 | (3) |
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106 | (2) |
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5.3 Immunological Applications |
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108 | (3) |
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6 Prediction of Cytotoxic T Cell (MHC Class I) Epitopes |
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111 | (24) |
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6.1 Background and Historical Overview of Methods for Pep-tide MHC Binding Prediction |
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112 | (2) |
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6.2 MHC Class I Epitope Binding Prediction Trained on Small Data Sets |
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114 | (6) |
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6.3 Prediction of CTL Epitopes by Neural Network Methods |
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120 | (13) |
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6.4 Summary of the Prediction Approach |
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133 | (2) |
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7 Antigen Processing in the MHC Class I Pathway |
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135 | (22) |
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135 | (2) |
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7.2 Evolution of the Immunosubunits |
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137 | (2) |
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7.3 Specificity of the (Immuno)Proteasome |
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139 | (4) |
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7.4 Predicting Proteasome Specificity |
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143 | (4) |
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7.5 Comparison of Proteasomal Prediction Performance |
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147 | (2) |
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7.6 Escape from Proteasomal Cleavage |
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149 | (1) |
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7.7 Post-Proteasomal Processing of Epitopes |
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150 | (3) |
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7.8 Predicting the Specificity of TAP |
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153 | (1) |
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7.9 Proteasome and TAP Evolution |
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154 | (3) |
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8 Prediction of Helper T Cell (MHC Class II) Epitopes |
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157 | (18) |
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158 | (1) |
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8.2 The Gibbs Sampler Method |
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159 | (13) |
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8.3 Further Improvements of the Approach |
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172 | (3) |
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9 Processing of MHC Class II Epitopes |
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175 | (12) |
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9.1 Enzymes Involved in Generating MHC Class II Ligands |
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176 | (3) |
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9.2 Selective Loading of Peptides to MHC Class II Molecules |
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179 | (1) |
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9.3 Phylogenetic Analysis of the Lysosomal Proteases |
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180 | (2) |
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9.4 Signs of the Specificities of Lysosomal Proteases on MHC Class II Epitopes |
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182 | (1) |
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9.5 Predicting the Specificity of Lysosomal Enzymes |
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182 | (5) |
10 B Cell Epitopes |
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187 | (16) |
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188 | (3) |
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10.2 Recognition of Antigen by B cells |
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191 | (10) |
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10.3 Neutralizing Antibodies |
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201 | (2) |
11 Vaccine Design |
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203 | (12) |
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11.1 Categories of Vaccines |
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204 | (3) |
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11.2 Polytope Vaccine: Optimizing Plasmid Design |
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207 | (2) |
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11.3 Therapeutic Vaccines |
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209 | (4) |
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213 | (2) |
12 Web-Based Tools for Vaccine Design |
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215 | (8) |
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12.1 Databases of MHC Ligands |
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215 | (2) |
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217 | (6) |
13 MHC Polymorphism |
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223 | (20) |
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13.1 What Causes MHC Polymorphism? |
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223 | (2) |
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225 | (18) |
14 Predicting Immunogenicity: An Integrative Approach |
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243 | (11) |
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14.1 Combination of MHC and Proteasome Predictions |
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244 | (1) |
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14.2 Independent Contributions from TAP and Proteasome Predictions |
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245 | (2) |
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14.3 Combinations of MHC, TAP, and Proteasome Predictions |
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247 | (4) |
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14.4 Validation on HIV Data Set |
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251 | (1) |
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14.5 Perspectives on Data Integration |
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252 | (2) |
References |
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254 | (37) |
Index |
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291 | |