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Part I Introduction and Background |
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1 Introduction to Systems Approaches to Cancer |
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3 | (26) |
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1.1 Cancer and Systems Approaches |
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3 | (6) |
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1.1.1 Nature and Causes of Cancer |
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3 | (1) |
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1.1.2 The Progression of Cancer |
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4 | (1) |
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1.1.3 Cancer: Clinical Background and Key Challenges |
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5 | (1) |
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1.1.4 Systems Biology Approaches to Cancer |
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6 | (1) |
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1.1.5 Key Books and Reviews of Systems Approaches |
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7 | (2) |
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1.1.6 Importance of Legal and Ethical Considerations |
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9 | (1) |
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1.2 Laboratory, Clinical, Data and Educational Resources |
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9 | (3) |
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1.2.1 Global Molecular and Cellular Measurement Technologies |
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9 | (1) |
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1.2.2 Cell Lines, Tissue Samples, Model Organisms, Biobanks |
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10 | (1) |
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1.2.3 Expression and Genetic Variation Databases for Cancer Research |
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11 | (1) |
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1.2.4 Education and Research Infrastructures |
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11 | (1) |
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1.3 Bioinformatics and Systems Biology Analysis |
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12 | (5) |
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1.3.1 Mathematical Tools in Cancer Signalling |
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12 | (1) |
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1.3.2 Computational Tools |
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13 | (1) |
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1.3.3 The Hallmarks of Cancer Revisited Through Modelling |
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14 | (1) |
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1.3.4 Analysis of Cell Death Pathways in Cancer: The Role of Collaborative and Interdisciplinary Research |
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14 | (2) |
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1.3.5 Approaches to Cancer Progression Outcomes |
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16 | (1) |
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1.3.6 Modelling at the Physiological and Tumour Level |
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16 | (1) |
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1.4 Diagnosis and Treatment Applications |
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17 | (4) |
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1.4.1 Diagnostic and Prognostic Cancer Biomarkers |
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17 | (1) |
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1.4.2 Cancer Drug Development |
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18 | (1) |
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1.4.3 Cancer Chronotherapy |
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19 | (1) |
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1.4.4 Clinical Applications of Systems Biology Approaches |
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19 | (1) |
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1.4.5 Cancer Robustness and Therapy Strategies |
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20 | (1) |
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1.5 Perspectives and Conclusions |
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21 | (1) |
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21 | (1) |
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21 | (1) |
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22 | (7) |
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2 Cancer: Clinical Background and Key Challenges |
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29 | (68) |
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29 | (2) |
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2.2 Pathology Integration in Cancer Biology Systems |
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31 | (15) |
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2.2.1 Definition of a Neoplasm |
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32 | (1) |
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2.2.2 Tumour Nomenclature |
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33 | (2) |
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35 | (3) |
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2.2.4 Growth Rate of a Tumour |
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38 | (1) |
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2.2.5 Dysplasia and Carcinoma in situ |
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39 | (2) |
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41 | (1) |
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42 | (3) |
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2.2.8 Cytology and Diagnosis |
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45 | (1) |
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2.3 Technological Approaches to Morphology and Pathology |
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46 | (3) |
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2.3.1 Hematoxylin-Eosin (H&E) Staining in Histological Diagnosis |
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46 | (1) |
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2.3.2 Immunohistochemistry |
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46 | (1) |
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2.3.3 Electron Microscopy |
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47 | (1) |
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2.3.4 Tissue Microarray (TMA) |
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48 | (1) |
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49 | (11) |
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50 | (1) |
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50 | (1) |
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51 | (7) |
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2.4.4 Treatment Strategies |
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58 | (2) |
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2.5 Major Cancers, Diagnosis, Disease-specific Supplementary Classifications, and Treatment Implications |
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60 | (21) |
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60 | (3) |
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63 | (6) |
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69 | (4) |
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2.5.4 Small Round Cell Tumours (SRCT) |
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73 | (4) |
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2.5.5 Leukaemias and Lymphomas |
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77 | (4) |
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2.6 Systems Biology of Cancer: Key Challenges for the Future |
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81 | (3) |
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84 | (1) |
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84 | (13) |
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Part II Laboratory, Clinical, Data and Educational Resources |
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3 Global Molecular and Cellular Measurement Technologies |
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97 | (30) |
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3.1 Introduction---The Need for Systems Biology Predictive Models |
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99 | (1) |
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100 | (2) |
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3.3 Analysis of the Genome |
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102 | (8) |
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102 | (1) |
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3.3.2 Next Generation Sequencing (NGS) |
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102 | (8) |
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110 | (5) |
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3.4.1 Two-dimensional Gel Electrophoresis |
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110 | (1) |
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111 | (1) |
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3.4.3 Quantitative Protein Arrays |
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112 | (1) |
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3.4.4 Immunohistochemistry |
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113 | (1) |
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114 | (1) |
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115 | (1) |
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115 | (5) |
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3.5.1 RNA Interferences (RNAi) |
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116 | (1) |
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117 | (1) |
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3.5.3 Determining Drug and Compound Action |
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118 | (1) |
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3.5.4 Protein-Protein and Protein-DNA Interactions |
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119 | (1) |
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3.6 Overall Determining Factors and Future Outlook |
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120 | (1) |
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121 | (1) |
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121 | (6) |
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4 Cell Lines, Tissue Samples, Model Organisms, and Biobanks: Infrastructure and Tools for Cancer Systems Biology |
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127 | (26) |
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127 | (1) |
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128 | (3) |
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4.2.1 The NCI-60 Human Cancer Cell Line Panel |
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129 | (2) |
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131 | (10) |
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4.3.1 Transgenic Mouse Models |
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132 | (2) |
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4.3.2 Chemically Induced Models |
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134 | (1) |
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4.3.3 Human Lung Tumour Xenografts |
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134 | (3) |
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4.3.4 Lung Cancer Models in Cancer Drug Development |
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137 | (1) |
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4.3.5 Models for the Study of Lung Cancer Metastasis |
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138 | (1) |
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4.3.6 Model Organisms: New Systems for Modelling Cancer |
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139 | (2) |
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4.3.7 Restrictions on the Use of Animals in Research |
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141 | (1) |
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141 | (3) |
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4.4.1 Paraffin Embedded Tissues |
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142 | (1) |
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4.4.2 Snap-frozen Tissues |
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142 | (1) |
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4.4.3 Linking Molecular and Clinical Measurements |
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143 | (1) |
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4.5 Role of Interactome Maps and Crucial Pathways |
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144 | (2) |
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4.5.1 Links to Specific Types of Cancer |
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144 | (1) |
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4.5.2 Synthetic Lethality as a Network-derived Treatment Success-story |
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145 | (1) |
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4.6 Integration into Systems and Computational Approaches |
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146 | (1) |
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4.7 The Future: Data Integration to Systems-level Experiments |
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147 | (1) |
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147 | (6) |
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5 Expression and Genetic Variation Databases for Cancer Research |
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153 | (12) |
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153 | (1) |
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154 | (5) |
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155 | (1) |
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5.2.2 Databases of Structural Variants |
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155 | (1) |
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5.2.3 Databases for Disease-causing Variants |
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156 | (1) |
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5.2.4 Large-scale Repositories for Experiments |
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157 | (1) |
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158 | (1) |
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159 | (2) |
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5.3.1 Archives of Gene Expression Data |
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159 | (1) |
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5.3.2 Added-value Databases |
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160 | (1) |
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5.4 Informatics Coordination by International Consortia |
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161 | (2) |
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163 | (2) |
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6 Education and Research Infrastructures |
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165 | (20) |
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165 | (2) |
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167 | (5) |
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6.2.1 Molecular and Cell Biologists |
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167 | (1) |
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6.2.2 Chemical Biologists |
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168 | (1) |
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6.2.3 Clinical Oncology Researchers |
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169 | (2) |
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171 | (1) |
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6.3 Training and Education of the Stakeholders |
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172 | (5) |
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6.3.1 The Core Subjects in Modern Scientific Education |
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172 | (2) |
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174 | (2) |
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176 | (1) |
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6.4 Organization of Cancer Research Centres and their Cross-disciplinary Activities |
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177 | (3) |
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180 | (1) |
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180 | (1) |
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180 | (5) |
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Part III Bioinformatics and Systems Biology Analysis |
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7 Mathematical Tools in Cancer Signalling Systems Biology |
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185 | (28) |
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185 | (3) |
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188 | (18) |
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7.2.1 When to Employ a Systems Biology Approach |
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188 | (2) |
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7.2.2 Biological Hypothesis and Set-up of the Signalling System |
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190 | (2) |
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7.2.3 Mathematical Modelling |
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192 | (4) |
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7.2.4 Experimental Techniques Used for Producing Quantitative Data |
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196 | (3) |
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7.2.5 Model Calibration: Parameter Estimation and Model Refinement |
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199 | (1) |
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200 | (4) |
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204 | (2) |
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206 | (2) |
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208 | (1) |
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208 | (1) |
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209 | (4) |
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8 Computational Tools for Systems Biology |
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213 | (32) |
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213 | (3) |
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8.2 Standards in Systems Biology |
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216 | (7) |
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8.2.1 Standards Support Communication in Biological Research |
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216 | (1) |
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217 | (5) |
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222 | (1) |
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223 | (5) |
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8.3.1 JWS Online and BioModels Database |
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224 | (1) |
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225 | (1) |
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226 | (1) |
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226 | (1) |
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227 | (1) |
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227 | (1) |
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228 | (6) |
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8.4.1 Tools for Model Formulation and Simulation |
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229 | (2) |
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8.4.2 Spatial and Temporal Simulation |
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231 | (1) |
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8.4.3 Boolean and Logical Models |
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231 | (1) |
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8.4.4 General Purpose Tools |
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232 | (2) |
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234 | (1) |
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235 | (2) |
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237 | (1) |
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237 | (1) |
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238 | (1) |
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238 | (7) |
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9 The Hallmarks of Cancer Revisited Through Systems Biology and Network Modelling |
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245 | (22) |
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245 | (2) |
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9.1.1 Hallmarks of Cancer |
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245 | (1) |
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246 | (1) |
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9.1.3 Advances in Hallmark Analysis and Networks |
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246 | (1) |
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9.2 The Potential of Systems Approaches to Disease |
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247 | (4) |
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9.2.1 Principles of Systems Biology |
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247 | (1) |
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9.2.2 Challenges in Modelling Networks in Cancer |
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248 | (1) |
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9.2.3 Network Inference Through Machine Learning |
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249 | (1) |
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9.2.4 An Illustrative Example: Systems Biology of Prion Disease |
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250 | (1) |
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9.3 Transcription and Protein Interaction Networks Revealed by Modular Cancer Biomarkers |
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251 | (1) |
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9.3.1 Networks and Biomarkers |
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251 | (1) |
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9.3.2 Proteomics and Pathways |
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251 | (1) |
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9.4 Growth, Proliferation and Apoptosis Revisited Through Signalling Network Modelling |
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252 | (2) |
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9.4.1 Signalling Pathways |
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252 | (1) |
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9.4.2 Growth Factors and Apoptosis |
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253 | (1) |
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9.5 Sustained Angiogenesis and Metastasis Revisited Through Multiscale Modelling |
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254 | (1) |
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9.5.1 Mathematical Modelling |
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254 | (1) |
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254 | (1) |
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254 | (1) |
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9.6 The Hallmarks of Cancer Extended to the Control of Metabolism and Stress |
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255 | (1) |
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9.6.1 Cancer as a Metabolic Disease |
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255 | (1) |
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9.6.2 Beyond Oncogene Addiction |
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256 | (1) |
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9.7 Conclusions and Perspectives |
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256 | (3) |
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9.7.1 Genome Variation and Instability Revisited Through Genetic and Genomic Networks |
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256 | (1) |
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9.7.2 Novel Avenues for Diagnosis, Therapy and Disease Network Modelling |
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257 | (1) |
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9.7.3 Frontier Challenges: Multiscale Integration and Cross-disciplinarity |
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258 | (1) |
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259 | (1) |
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259 | (8) |
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10 Systems Biology Analysis of Cell Death Pathways in Cancer: How Collaborative and Interdisciplinary Research Helps |
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267 | (30) |
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268 | (1) |
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269 | (6) |
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10.2.1 The Death Receptor-mediated Pathway |
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271 | (1) |
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10.2.2 The Mitochondria-mediated Pathway |
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271 | (1) |
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10.2.3 Modulators of Caspase Activity: The IAP Family of Proteins and Their Regulators |
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272 | (1) |
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10.2.4 Cross-talk Between Various Modes of Cell Death |
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273 | (2) |
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10.3 Dysregulation of Cell Death Pathways in Cancer |
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275 | (5) |
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10.3.1 Defects in the Apoptotic Machinery of Tumour Cells |
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275 | (4) |
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10.3.2 Defects in Autophagy-regulated Machinery |
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279 | (1) |
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10.4 Mathematical Modelling of Cell Death Pathways |
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280 | (5) |
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10.4.1 Different Models of Cell Death |
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280 | (2) |
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10.4.2 Modelling Cell-fate Decision Between Survival, Apoptosis and Necrosis |
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282 | (3) |
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10.5 Elements for Interdisciplinary Approaches to Cancer Research |
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285 | (3) |
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10.5.1 Cancers Susceptible to Integrated Systems Approaches |
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285 | (1) |
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10.5.2 Laboratory and Clinical Measurements and Resources |
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286 | (2) |
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10.6 How to Share Knowledge About Systems Biology Approaches to Cancers (See Also Chap. 6) |
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288 | (2) |
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288 | (1) |
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10.6.2 Visualizing Networks as a Stimulus to Reasoning and Exchanges |
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289 | (1) |
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10.6.3 Sharing Network Description and Models |
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290 | (1) |
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10.7 Major Collaborative Efforts |
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290 | (1) |
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10.7.1 Apo-sys: Large Scale Collaborative Research on Apoptosis |
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290 | (1) |
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10.7.2 Cancersys: Medium Scale Collaborative Research on Hepatocellular Carcinoma |
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291 | (1) |
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10.8 Supporting Collaborative Research Projects |
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291 | (1) |
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10.8.1 Enfin: Systems Biology Tool Development and Application to Cancer |
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291 | (1) |
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10.8.2 Gen2phen: Bioinformatics Analysis of Genetic Variation and Application to Colorectal Cancer |
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292 | (1) |
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292 | (1) |
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293 | (1) |
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293 | (4) |
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11 Systems Biology, Bioinformatics and Medicine Approaches to Cancer Progression Outcomes |
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297 | (12) |
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11.1 Introduction: The Concept of Pathway Signatures |
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297 | (2) |
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11.2 Identification of Biological Motifs from Gene Array Data |
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299 | (4) |
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11.2.1 Gene Expression Profiling |
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299 | (2) |
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11.2.2 Metagenes for Clusters of Co-regulated Genes |
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301 | (2) |
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11.3 From Biological Motifs to Pathway Activation |
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303 | (1) |
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11.4 How Realistic is Modelling of Carcinogenesis and Tumour Development in Virtual Tissues and Organs? |
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304 | (3) |
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11.4.1 Spatial-temporal Models of Tumours |
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304 | (1) |
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11.4.2 Tumour Modelling Perspectives |
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305 | (2) |
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307 | (2) |
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12 System Dynamics at the Physiological and Tumour Level |
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309 | (20) |
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12.1 Introduction to Mathematical Modelling in Cancer |
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309 | (2) |
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12.1.1 Lessons from History |
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309 | (2) |
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12.1.2 Extension to Bioinformatics and Systems Biology |
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311 | (1) |
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12.2 Mathematical Models in Cancer |
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311 | (3) |
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12.2.1 The Role of Modelling in Cancer Research |
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311 | (2) |
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12.2.2 Aspects of Cancer Modelling |
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313 | (1) |
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314 | (1) |
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12.3.1 Historical Perspective---Understanding Tumour as a Complex System |
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314 | (1) |
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12.3.2 Building the Tumour System---Starting with Spheroids |
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314 | (1) |
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12.4 Iterative Modelling of Tumour Systems |
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315 | (2) |
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12.5 Experimental Studies of Tumour Invasion |
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317 | (1) |
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12.6 Tumour Modelling Collaborations |
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318 | (3) |
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12.7 Detailed Modelling Example |
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321 | (4) |
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12.7.1 Carcinogenesis Transitions |
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321 | (2) |
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323 | (2) |
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325 | (1) |
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325 | (4) |
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Part IV Diagnosis, Clinical and Treatment Applications |
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13 Diagnostic and Prognostic Cancer Biomarkers: From Traditional to Systems Approaches |
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329 | (38) |
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329 | (2) |
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331 | (1) |
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13.3 Biomarkers for Prediction of Response to Treatment |
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331 | (2) |
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13.3.1 The ErbB Family of Receptor Tyrosine Kinases: HER2 as a Predictive Marker in Breast Cancer |
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331 | (1) |
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13.3.2 EGFR in Head and Neck, Colorectal and Non-small-cell Lung Cancers |
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332 | (1) |
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13.4 Biomarkers for Prognosis |
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333 | (7) |
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13.4.1 Traditional Clinical Markers---Lymph Node Involvement |
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333 | (1) |
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13.4.2 Histological Grade and Proliferation |
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333 | (1) |
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13.4.3 Gene Expression Grade |
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334 | (1) |
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13.4.4 Proliferation Markers |
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334 | (1) |
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13.4.5 Hypoxia Biomarkers |
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334 | (1) |
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13.4.6 Global and Multi-gene Expression Profiling |
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335 | (5) |
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13.4.7 New Areas for Biomarker Development---microRNA |
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340 | (1) |
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13.4.8 Chromosome Aberration |
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340 | (1) |
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13.5 Biomarkers for Monitoring |
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340 | (1) |
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341 | (1) |
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13.5.2 Mutated Plasma DNA |
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341 | (1) |
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13.6 Measurement and Analysis of Biomarkers |
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341 | (3) |
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13.6.1 Key Measurement Technologies |
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342 | (1) |
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342 | (1) |
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343 | (1) |
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343 | (1) |
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13.7 Identification, Standardization and Validation of Effective Biomarkers |
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344 | (3) |
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13.8 Annotated High-quality Clinical Samples |
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347 | (1) |
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13.9 Analyses and Simulations to Predict and Identify Biomarkers |
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348 | (1) |
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13.10 Approaches to Data Analyses in Genomic Studies |
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348 | (5) |
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13.10.1 Class Discovery and Class Prediction |
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348 | (2) |
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13.10.2 Gene and Protein Networks |
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350 | (1) |
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13.10.3 Knowledge-based Class Comparison |
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351 | (1) |
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13.10.4 Knowledge-based Class Prediction and Mining of Genomic Data |
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351 | (1) |
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13.10.5 Literature Data-mining and Data Repositories |
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352 | (1) |
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13.11 Meta-analyses of Biomarker Studies |
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353 | (1) |
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13.12 Quantitative Simulations of Major Pathways Leading to Biomarker Development |
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353 | (2) |
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13.12.1 Simulation of Cancer Pathways: The EGFR Pathway |
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354 | (1) |
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13.12.2 Databases and Repositories of Models |
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355 | (1) |
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13.13 Pharmacokinetics and Pharmacodynamics |
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355 | (1) |
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13.14 Integrated Approaches to Biomarker Discovery and Development |
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356 | (2) |
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358 | (9) |
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14 Systems Biology Approaches to Cancer Drug Development |
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367 | (14) |
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367 | (3) |
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14.1.1 The Systems View of Drug Action |
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367 | (3) |
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14.1.2 Introducing Systems Biology into Drug Development |
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370 | (1) |
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370 | (2) |
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14.2.1 Linking Data to the Models |
|
|
370 | (2) |
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14.3 Case Studies of Modelling Cellular Networks |
|
|
372 | (3) |
|
14.3.1 Using Cellular Networks in Drug Development |
|
|
372 | (1) |
|
14.3.2 Modelling the Cellular Action of Seliciclib and Other cdk2 Inhibitors |
|
|
372 | (1) |
|
14.3.3 Apoptosis and Signal Transduction Pathways |
|
|
373 | (1) |
|
14.3.4 Difficulties with Detailed Modelling |
|
|
374 | (1) |
|
14.4 Modelling at Cellular Scales |
|
|
375 | (2) |
|
14.4.1 `Virtual Tumour' Model as a Simpler Approach |
|
|
375 | (1) |
|
14.4.2 Modelling Schedules and Combinations |
|
|
375 | (1) |
|
14.4.3 Predicting Schedules in Drug Development |
|
|
376 | (1) |
|
14.4.4 Chronotherapy and the TEMPO Project |
|
|
376 | (1) |
|
14.5 Technologies Typically Used at a Biotech Company |
|
|
377 | (1) |
|
14.5.1 Computing Requirements |
|
|
377 | (1) |
|
14.5.2 Model Database and Reports |
|
|
378 | (1) |
|
14.5.3 One Operational Example: Delivering the Outputs with ModelPlayer™ |
|
|
378 | (1) |
|
|
378 | (1) |
|
|
379 | (2) |
|
15 Circadian Rhythms and Cancer Chronotherapeutics |
|
|
381 | (28) |
|
|
|
|
15.1 Circadian Rhythms in Health and Diseases |
|
|
381 | (7) |
|
15.1.1 Biological Evidence |
|
|
382 | (4) |
|
15.1.2 Experimentally-based Computational Models |
|
|
386 | (2) |
|
15.2 Chronopharmacology, Chronotolerance and Chronoefficacy of Anticancer Drugs |
|
|
388 | (11) |
|
15.2.1 Experimental Evidence and Mechanisms |
|
|
388 | (3) |
|
15.2.2 Clinical Cancer Chronotherapeutics |
|
|
391 | (3) |
|
15.2.3 Probing Circadian Patterns of Anticancer Drug Delivery in silico |
|
|
394 | (5) |
|
15.3 From Standard to Personalized Cancer Chronotherapeutics |
|
|
399 | (5) |
|
15.3.1 Experimental and Clinical Data |
|
|
399 | (3) |
|
15.3.2 Insights from a Modelling Approach |
|
|
402 | (2) |
|
15.4 Conclusions and Perspectives |
|
|
404 | (1) |
|
|
405 | (1) |
|
|
405 | (4) |
|
16 Clinical Applications of Systems Biology Approaches |
|
|
409 | (20) |
|
|
|
|
|
|
409 | (4) |
|
16.2 Systems Biology Approaches to Identifying Diagnostic, Prognostic, and Therapeutic Biomarkers for Cancer |
|
|
413 | (4) |
|
16.2.1 Genomic, Transcriptomic, Proteomic, and Metabolic (Omics) Analysis of Human Tumours |
|
|
413 | (2) |
|
16.2.2 Computational Mining of Omics Data |
|
|
415 | (2) |
|
16.3 Systems Biology Approaches to the Design of Combinatorial Targeted Therapy for Cancer |
|
|
417 | (6) |
|
16.3.1 Animal and Cell Line Models |
|
|
417 | (2) |
|
16.3.2 Pharmacodynamic Modelling |
|
|
419 | (1) |
|
16.3.3 Pharmacokinetic Modelling |
|
|
420 | (1) |
|
16.3.4 Combined Pharmacodynamic-Pharmacokinetic Modelling |
|
|
420 | (1) |
|
16.3.5 Combined Therapy Modelling |
|
|
421 | (1) |
|
16.3.6 Biopsy and Virtual Biopsy Approaches to Measuring Tumours and Assessing Treatment Activity |
|
|
422 | (1) |
|
16.4 The Future of Clinical Trials: Applying Systems Approaches to Clinical Trial Design |
|
|
423 | (1) |
|
|
424 | (5) |
|
17 Cancer Robustness and Therapy Strategies |
|
|
429 | (20) |
|
|
|
429 | (3) |
|
17.1.1 Cancer as a Robust System |
|
|
429 | (1) |
|
17.1.2 What is Robustness? |
|
|
430 | (1) |
|
17.1.3 Robustness and Homeostasis |
|
|
431 | (1) |
|
17.2 Mechanisms for Robustness |
|
|
432 | (3) |
|
|
432 | (1) |
|
|
432 | (1) |
|
|
433 | (1) |
|
|
433 | (2) |
|
17.3 Mechanisms for Cancer Robustness |
|
|
435 | (1) |
|
17.4 Robustness Trade-offs |
|
|
436 | (1) |
|
17.5 Theoretically-motivated Therapeutic Strategies |
|
|
437 | (4) |
|
17.6 An Appropriate Index of Treatment Efficacy |
|
|
441 | (1) |
|
|
441 | (2) |
|
|
443 | (1) |
|
|
444 | (1) |
|
|
444 | (5) |
|
Part V Perspectives and Conclusions |
|
|
|
18 Synthetic Biology and Perspectives |
|
|
449 | (22) |
|
|
|
|
449 | (1) |
|
18.2 Synthetic Biology for Cancer Research and Applications |
|
|
450 | (4) |
|
18.2.1 Introduction to Synthetic Biology |
|
|
450 | (1) |
|
18.2.2 Manipulation at the Molecular Level |
|
|
451 | (1) |
|
18.2.3 Applications in Cells |
|
|
452 | (1) |
|
18.2.4 Synthetic Biology in Japan |
|
|
453 | (1) |
|
18.3 Synthetic Biology Applications to Cancer |
|
|
454 | (4) |
|
|
454 | (1) |
|
|
455 | (1) |
|
|
455 | (1) |
|
18.3.4 Gene/Protein Therapy |
|
|
456 | (1) |
|
|
457 | (1) |
|
18.4 Review Articles and Workshops---Integrated Perspectives |
|
|
458 | (5) |
|
18.4.1 How Systems Biology Can Advance Cancer Research |
|
|
459 | (1) |
|
18.4.2 Cancer Systems Biology---2nd Workshop |
|
|
460 | (2) |
|
18.4.3 Systems Medicine: The Future of Medical Genomics and Healthcare |
|
|
462 | (1) |
|
18.5 Resources Needed to Support Systems Approaches to Cancer Research and Diagnosis |
|
|
463 | (2) |
|
18.5.1 Infrastructure Requirements for Systems Biology |
|
|
463 | (1) |
|
18.5.2 Clinical Resources |
|
|
464 | (1) |
|
18.5.3 Data Resources, Analysis and Cancer Modelling Tools |
|
|
464 | (1) |
|
|
465 | (1) |
|
|
465 | (6) |
|
|
471 | (8) |
|
|
|
|
471 | (5) |
|
19.1.1 Introduction and Background |
|
|
471 | (1) |
|
19.1.2 Laboratory, Clinical, Data and Educational Resources for Cancer Research |
|
|
472 | (1) |
|
19.1.3 Bioinformatics and Systems Biology Research Results |
|
|
473 | (2) |
|
19.1.4 Translation to Clinical Applications |
|
|
475 | (1) |
|
|
476 | (1) |
|
|
476 | (3) |
Index |
|
479 | |