New Approaches to in silico Design of Epitope-Based Vaccines

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106 APPENDIX B. EPITOPE DISCOVERY Algorithm B.1: Weighted majority voting. This voting is applied to resolve conflicts in results of microarray experiments. Based on the number of absent, marginal and present assignments a protein is unambiguously assigned to exactly one class. The marginal classifications were weighted only 2/3 to achieve a better separation in absent and present, reducing the number of marginal assignments. a ⇐ number of absent calls m ⇐ number of marginal calls p ⇐ number of present calls sum ⇐ a + 2 3m + p label ⇐ unassigned if a/sum > 2 3 label ⇐ absent else if p/sum > 2 3 then label ⇐ present else label ⇐ marginal endif endif then
Appendix C Applications Table C.1: List of all MHC alleles used in the study on designing string-of-beads vaccines. The employed probabilities and BIMAS score thresholds are given. MHC allele Threshold Probability (in %) HLA-A*01:01 0.087 4.50 HLA-A*02:01 1.257 10.69 HLA-A*02:05 0.418 0.88 HLA-A*03:01 0.055 3.69 HLA-A*11:01 0.044 7.52 HLA-A*24:02 1.158 12.91 HLA-A*31:01 0.0964 2.43 HLA-A*68:01 2.399 1.77 HLA-B*04:01 5.224 1.55 HLA-B*07:02 1.623 3.61 HLA-B*08:01 0.072 2.95 HLA-B*15:01 0.217 2.06 HLA-B*27:02 50.043 0.15 HLA-B*27:05 163.868 1.11 HLA-B*35:01 3.202 3.24 HLA-B*37:01 1.424 0.44 HLA-B*38:01 7.929 0.66 HLA-B*39:01 4.734 1.77 HLA-B*40:01 21.178 5.31 HLA-B*40:06 1.044 0.52 HLA-B*44:03 6.209 2.21 HLA-B*51:01 39.207 3.24 HLA-B*51:02 155.050 0.22 HLA-B*52:01 8.589 0.88 HLA-B*58:01 13.356 2.65 HLA-Cw*04:01 4.061 8.26 HLA-Cw*06:02 2.169 5.09 HLA-Cw*07:02 62.183 9.66 107
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New Approaches to in silico Design
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Abstract Traditional trial-and-erro
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Acknowledgments First of all, I wou
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Contents 1 Introduction 1 2 Biologi
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7.3 Design of String-of-Beads Vacci
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Chapter 1 Introduction Motivation T
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prediction of T-cell epitopes is a
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systemic property but employ peptid
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Chapter 2 Biological Background In
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2.2. CELLULAR IMMUNE RESPONSE 9 A B
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2.2. CELLULAR IMMUNE RESPONSE 11 [3
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2.2. CELLULAR IMMUNE RESPONSE 13 Ho
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2.3. VACCINES 15 the immune system
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Chapter 3 Algorithmic Background In
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3.1. COMBINATORIAL OPTIMIZATION 19
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3.2. MACHINE LEARNING 21 Figure 3.2
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Chapter 4 Epitope Discovery After h
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4.1. INTRODUCTION 31 Figure 4.2: Sc
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4.2. IMPROVED KERNELS FOR MHC BINDI
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4.3. MHC BINDING PREDICTION FOR ALL
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4.4. T-CELL EPITOPE PREDICTION 45 T
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4.4. T-CELL EPITOPE PREDICTION 47 i
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4.4. T-CELL EPITOPE PREDICTION 49 F
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4.4. T-CELL EPITOPE PREDICTION 51 t
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Chapter 5 Epitope Selection The pre
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Page 69 and 70: 5.3. MATHEMATICAL ABSTRACTION 57 fo
Page 71 and 72: 5.4. EXPERIMENTAL RESULTS 59 Let G
Page 73 and 74: 5.6. IMPLEMENTATION 61 number of no
Page 75 and 76: Chapter 6 Epitope Assembly The math
Page 77 and 78: 6.2. APPROACH 65 Figure 6.2: Graph
Page 79 and 80: 6.3. INCORPORATION OF PROTEASOMAL C
Page 81 and 82: 6.4. EXPERIMENTAL RESULTS 69 Figure
Page 83 and 84: 6.5. DISCUSSION 71 epitope sets wit
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Page 87 and 88: 7.1. OPTITOPE - A WEB SERVER FOR EP
Page 89 and 90: 7.2. DESIGN OF A PEPTIDE COCKTAIL V
Page 95 and 96: 7.3. DESIGN OF STRING-OF-BEADS VACC
Page 105 and 106: Chapter 8 Discussion & Conclusion P
Page 107 and 108: selection of an optimal epitope set
Page 109 and 110: Appendix A Abbreviations AA Amino a
Page 111 and 112: Appendix B Epitope Discovery Table
Page 113 and 114: Table B.3: Regression performances
Page 115 and 116: Table B.5: Overall performance of M
Page 117: Table B.7: Gene expression omnibus
Page 121 and 122: Appendix D Contributions Chapter 4
Page 123 and 124: Appendix E Publications Published M
Page 125 and 126: Bibliography [1] M. Moutschen, P. L
Page 127 and 128: BIBLIOGRAPHY 115 [19] T. Sturniolo,
Page 129 and 130: BIBLIOGRAPHY 117 [44] F. Morein and
Page 131 and 132: BIBLIOGRAPHY 119 [72] C. Widmer, N.
Page 133 and 134: BIBLIOGRAPHY 121 [97] H. Parkinson,
Page 135 and 136: BIBLIOGRAPHY 123 [123] NCBI dbMHC d
Page 137 and 138: BIBLIOGRAPHY 125 [150] World Health
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New Approaches to in silico Design of Epitope-Based Vaccines

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