Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application, and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. This book discusses big data text-based mining to better understand the molecular architecture of diseases and to guide health care decision.
It will be a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in understanding more about how to process and apply great amounts of data to improve their research.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Dedication - p. v 6. Contributors - p. xi 7. Foreword I - p. xv 8. Foreword II - p. xvii 9. Preface - p. xxi 10. Acknowledgments - p. xxiii 11. Chapter 1: Comprehensive Workflow for Integrative Transcriptomics Meta-Analysis - p. xxiii 12. Chapter 2: Proteomics and Protein Interaction in Molecular Cell Signaling Pathways - p. 16 13. Chapter 3: Understanding Specialized Ribosomal Protein Functions and Associated Ribosomopathies by Navigating Across Sequence, Literature, and Phenotype Information Resources - p. 34 14. Chapter 4: Big Data, Artificial Intelligence, and Machine Learning in Neurotrauma - p. 51 15. Chapter 5: Artificial Intelligence Integration for Neurodegenerative Disorders - p. 75 16. Chapter 6: Robust Detection of Epilepsy Using Weighted-Permutation Entropy: Methods and Analysis - p. 89 17. Chapter 7: Biological Knowledge Graph Construction, Search, and Navigation - p. 106 18. Chapter 8: Healthcare Decision-Making Support Based on the Application of Big Data to Electronic Medical Records: A Knowledge Management Cycle - p. 120 19. Chapter 9: Computational Modeling in Global Infectious Disease Epidemiology - p. 131 20. Chapter 10: Semiautomatic Annotator for Medical NLP Applications: About the Tool - p. 141 21. Chapter 11: Intractome Curation and Analysis for Stroke and Spinal Cord Injury Using Semiautomatic Annotations - p. 150 22. Chapter 12: Deep Genomics and Proteomics: Language Model-Based Embedding of Biological Sequences and Their Applications in Bioinformatics - p. 166 23. Chapter 13: In Silico Transcription Factor Discovery via Bioinformatics Approach: Application on iPSC Reprogramming Resistant Genes - p. 181 24. Index - p. 195
Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application, and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. This book discusses big data text-based mining to better understand the molecular architecture of diseases and to guide health care decision.
It will be a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in understanding more about how to process and apply great amounts of data to improve their research.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Dedication - p. v 6. Contributors - p. xi 7. Foreword I - p. xv 8. Foreword II - p. xvii 9. Preface - p. xxi 10. Acknowledgments - p. xxiii 11. Chapter 1: Comprehensive Workflow for Integrative Transcriptomics Meta-Analysis - p. xxiii 12. Chapter 2: Proteomics and Protein Interaction in Molecular Cell Signaling Pathways - p. 16 13. Chapter 3: Understanding Specialized Ribosomal Protein Functions and Associated Ribosomopathies by Navigating Across Sequence, Literature, and Phenotype Information Resources - p. 34 14. Chapter 4: Big Data, Artificial Intelligence, and Machine Learning in Neurotrauma - p. 51 15. Chapter 5: Artificial Intelligence Integration for Neurodegenerative Disorders - p. 75 16. Chapter 6: Robust Detection of Epilepsy Using Weighted-Permutation Entropy: Methods and Analysis - p. 89 17. Chapter 7: Biological Knowledge Graph Construction, Search, and Navigation - p. 106 18. Chapter 8: Healthcare Decision-Making Support Based on the Application of Big Data to Electronic Medical Records: A Knowledge Management Cycle - p. 120 19. Chapter 9: Computational Modeling in Global Infectious Disease Epidemiology - p. 131 20. Chapter 10: Semiautomatic Annotator for Medical NLP Applications: About the Tool - p. 141 21. Chapter 11: Intractome Curation and Analysis for Stroke and Spinal Cord Injury Using Semiautomatic Annotations - p. 150 22. Chapter 12: Deep Genomics and Proteomics: Language Model-Based Embedding of Biological Sequences and Their Applications in Bioinformatics - p. 166 23. Chapter 13: In Silico Transcription Factor Discovery via Bioinformatics Approach: Application on iPSC Reprogramming Resistant Genes - p. 181 24. Index - p. 195
