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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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3.2. MACHINE LEARNING 23 Figure 3.3
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3.2. MACHINE LEARNING 25 Figure 3.4
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3.2. MACHINE LEARNING 27 Figure 3.5
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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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4.2. IMPROVED KERNELS FOR MHC BINDI
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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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4.3. MHC BINDING PREDICTION FOR ALL
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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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5.3. MATHEMATICAL ABSTRACTION 55 im
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- Page 107 and 108: selection of an optimal epitope set
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- Page 117 and 118: Table B.7: Gene expression omnibus
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- 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