Contributors |
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xi | |
Foreword I |
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xv | |
Foreword II |
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xvii | |
Preface |
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xxi | |
Acknowledgments |
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xxiii | |
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1 Comprehensive Workflow for Integrative Transcriptomics Meta-analysis |
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1 | (16) |
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1 | (1) |
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1 | (9) |
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3 Crossplatform integration |
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10 | (2) |
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12 | (1) |
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13 | (1) |
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13 | (4) |
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2 Proteomics and Protein Interaction in Molecular Cell Signaling Pathways |
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17 | (18) |
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17 | (1) |
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2 Experimental techniques |
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17 | (2) |
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19 | (12) |
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31 | (1) |
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31 | (4) |
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3 Understanding Specialized Ribosomal Protein Functions and Associated Ribosomopathies by Navigating Across Sequence, Literature, and Phenotype Information Resources |
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35 | (18) |
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35 | (1) |
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36 | (2) |
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3 Specialized functions of RPs |
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38 | (2) |
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4 Exploring RP roles in health and disease by navigating bioinformatics resources |
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40 | (7) |
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5 Conclusions and future directions |
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47 | (6) |
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47 | (6) |
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4 Big Data, Artificial Intelligence, and Machine Learning in Neurotrauma |
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53 | (24) |
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53 | (1) |
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2 Big data: Characteristics, definitions, and examples |
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53 | (3) |
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3 Traumatic brain injury (TBI) |
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56 | (1) |
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57 | (3) |
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60 | (1) |
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6 Artificial intelligence |
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60 | (1) |
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61 | (1) |
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8 Examples of using BD approaches in TBI research |
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62 | (1) |
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62 | (2) |
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64 | (2) |
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66 | (2) |
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12 Future of big data in TBI |
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68 | (9) |
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69 | (8) |
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5 Artificial Intelligence Integration for Neurodegenerative Disorders |
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77 | (14) |
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77 | (3) |
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2 Wearables and ML-based therapeutics |
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80 | (1) |
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3 Neurodegenerative therapeutics through Al |
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80 | (5) |
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4 Al-based clinical decision-making |
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85 | (1) |
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5 Limitations and future perspectives |
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86 | (5) |
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86 | (5) |
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6 Robust Detection of Epilepsy Using Weighted-Permutation Entropy: Methods and Analysis |
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91 | (16) |
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91 | (1) |
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92 | (3) |
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3 Experiments and results |
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95 | (10) |
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105 | (1) |
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105 | (1) |
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106 | (1) |
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7 Biological Knowledge Graph Construction, Search, and Navigation |
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107 | (14) |
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1 Resource description format (RDF) knowledge graphs |
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107 | (3) |
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2 Our proposed approach to create, search, and visualize biological knowledge graphs |
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110 | (8) |
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3 Extending the knowledge graph using machine-learning techniques |
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118 | (1) |
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119 | (2) |
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119 | (2) |
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8 Healthcare Decision-Making Support Based on the Application of Big Data to Electronic Medical Records: A Knowledge Management Cycle |
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121 | (12) |
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121 | (1) |
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2 Knowledge management cycle in healthcare |
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121 | (6) |
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3 Key points for an EMR-based big data approach to clinical knowledge management |
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127 | (3) |
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130 | (1) |
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130 | (3) |
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9 Computational Modeling in Global Infectious Disease Epidemiology |
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133 | (10) |
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133 | (1) |
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2 Prediction of progression of bacterial resistance |
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134 | (3) |
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137 | (3) |
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140 | (1) |
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141 | (2) |
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10 Semiautomatic Annotator for Medical NLP Applications: About the Tool |
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143 | (8) |
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143 | (3) |
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2 The semiautomatic annotator |
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146 | (4) |
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150 | (1) |
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11 Intractome Curation and Analysis for Stroke and Spinal Cord Injury Using Semiautomatic Annotations |
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151 | (16) |
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1 Gene and protein accession and mapping process |
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151 | (1) |
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2 Article selection process |
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152 | (2) |
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3 Cooccurrence relation extraction process |
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154 | (1) |
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4 Systems biology relational extraction process |
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154 | (3) |
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157 | (2) |
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159 | (1) |
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160 | (1) |
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8 Stroke intractome network analysis results |
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160 | (2) |
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9 Spinal cord injury intractome construction and analysis |
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162 | (2) |
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164 | (1) |
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165 | (1) |
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165 | (2) |
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12 Deep Genomics and Proteomics: Language Model-Based Embedding of Biological Sequences and Their Applications in Bioinformatics |
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167 | (16) |
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167 | (4) |
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171 | (4) |
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175 | (3) |
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178 | (2) |
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180 | (3) |
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13 In Silico Transcription Factor Discovery via Bioinformatics Approach: Application on iPSC Reprogramming Resistant Genes |
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183 | (12) |
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183 | (1) |
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2 Regulation of reprogramming resistant genes (RRG) by iPSC reprogramming factors Oct4, Sox2, KLF4, NANOG, and c-Myc |
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184 | (5) |
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3 Comparative genomic analysis iPSC transcription regulation with SHOE |
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189 | (1) |
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189 | (2) |
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191 | (4) |
Index |
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195 | |