New Approaches to in silico Design of Epitope-Based Vaccines

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100 APPENDIX B. EPITOPE DISCOVERY Table B.2: Classification performances of WD-RBF and WD kernel. The table lists the mean auROCs obtained by a two-times nested five-fold cross validation of a WD-RBF (blosum50) and a WD kernel on the IEDB h9 allele subsets. The best performance per allele is highlighted (bold). Allele WD-RBF WD HLA-A*01:01 0.967 0.966 HLA-A*02:01 0.949 0.949 HLA-A*02:02 0.881 0.879 HLA-A*02:03 0.903 0.896 HLA-A*02:06 0.913 0.908 HLA-A*03:01 0.923 0.924 HLA-A*11:01 0.938 0.940 HLA-A*23:01 0.813 0.744 HLA-A*24:02 0.733 0.748 HLA-A*24:03 0.840 0.844 HLA-A*26:01 0.926 0.902 HLA-A*29:02 0.906 0.906 HLA-A*30:01 0.931 0.908 HLA-A*30:02 0.802 0.780 HLA-A*31:01 0.921 0.916 HLA-A*33:01 0.905 0.898 HLA-A*68:01 0.856 0.846 HLA-A*68:02 0.877 0.879 HLA-A*69:01 0.869 0.816 HLA-B*07:02 0.947 0.946 HLA-B*08:01 0.917 0.867 HLA-B*15:01 0.913 0.909 HLA-B*18:01 0.895 0.905 HLA-B*27:05 0.931 0.927 HLA-B*35:01 0.863 0.850 HLA-B*40:01 0.937 0.931 HLA-B*40:02 0.781 0.721 HLA-B*44:02 0.710 0.749 HLA-B*44:03 0.783 0.806 HLA-B*45:01 0.871 0.836 HLA-B*51:01 0.869 0.846 HLA-B*53:01 0.870 0.870 HLA-B*54:01 0.884 0.842 HLA-B*57:01 0.934 0.934 HLA-B*58:01 0.949 0.936
Table B.3: Regression performances of WD-RBF and WD kernel. The table lists the mean PCCs obtained by a two-times nested five-fold cross validation of a WD-RBF (blosum50) and a WD kernel on the IEDB h9 allele subsets. The best performance per allele is highlighted (bold). Allele WD-RBF WD HLA-A*01:01 0.706 0.705 HLA-A*02:01 0.815 0.810 HLA-A*02:02 0.770 0.765 HLA-A*02:03 0.793 0.770 HLA-A*02:06 0.770 0.753 HLA-A*03:01 0.742 0.733 HLA-A*11:01 0.802 0.793 HLA-A*23:01 0.686 0.609 HLA-A*24:02 0.620 0.617 HLA-A*24:03 0.587 0.599 HLA-A*26:01 0.578 0.561 HLA-A*29:02 0.840 0.799 HLA-A*30:01 0.672 0.668 HLA-A*30:02 0.574 0.581 HLA-A*31:01 0.757 0.753 HLA-A*33:01 0.672 0.671 HLA-A*68:01 0.739 0.736 HLA-A*68:02 0.756 0.749 HLA-A*69:01 0.533 0.518 HLA-B*07:02 0.796 0.786 HLA-B*08:01 0.533 0.512 HLA-B*15:01 0.709 0.713 HLA-B*18:01 0.761 0.752 HLA-B*27:05 0.714 0.684 HLA-B*35:01 0.668 0.658 HLA-B*40:01 0.557 0.561 HLA-B*40:02 0.748 0.734 HLA-B*44:02 0.614 0.653 HLA-B*44:03 0.792 0.799 HLA-B*45:01 0.742 0.706 HLA-B*51:01 0.708 0.725 HLA-B*53:01 0.737 0.743 HLA-B*54:01 0.744 0.727 HLA-B*57:01 0.742 0.698 HLA-B*58:01 0.742 0.709 101
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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
Page 61 and 62: 4.4. T-CELL EPITOPE PREDICTION 49 F
Page 63 and 64: 4.4. T-CELL EPITOPE PREDICTION 51 t
Page 65 and 66: Chapter 5 Epitope Selection The pre
Page 67 and 68: 5.3. MATHEMATICAL ABSTRACTION 55 im
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
Page 85 and 86: Chapter 7 Applications In the previ
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: Appendix B Epitope Discovery Table
Page 115 and 116: Table B.5: Overall performance of M
Page 117 and 118: Table B.7: Gene expression omnibus
Page 119 and 120: Appendix C Applications Table C.1:
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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