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Leveraging Biomedical and Healthcare Data : Semantics, Analytics and Knowledge /
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 influ...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Elsevier Ltd. : Academic Press,
2018.
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge; Copyright; Dedication; Contents; Contributors; Foreword I; Foreword II; Preface; Acknowledgments; Chapter 1: Comprehensive Workflow for Integrative Transcriptomics Meta-Analysis; 1. Introduction; 2. Data Preparation; 2.1. Data Download; 2.2. Quality Control; 2.2.1. Single-Array Metrics; 2.2.2. Multiarray Metrics; 2.3. Data Preprocessing; 2.3.1. Background Correction; 2.3.2. Normalization; 2.3.3. Summarization; 2.3.4. Comparison Between Different Procedures
- 2.4. Batch Effect: A Special Concern During Data Integration3. Crossplatform Integration; 4. Conclusion; References; Chapter 2: Proteomics and Protein Interaction in Molecular Cell Signaling Pathways; 1. Introduction; 2. Experimental Techniques; 3. Computational Methods; 3.1. Sequences Databases and Analysis; 3.2. Protein Feature Prediction Using Protein Sequence; 3.3. Structure Databases and Analysis; 3.4. Protein Families; 3.5. Interactions, Pathways, and System Biology; 3.6. Posttranslational Modifications; 4. Conclusion; References
- Chapter 3: Understanding Specialized Ribosomal Protein Functions and Associated Ribosomopathies by Navigating Across Sequ ... 1. Introduction; 2. RPs and Diseases; 2.1. Ribosomopathies; 2.2. Cancer; 3. Specialized Functions of RPs; 3.1. RP-Mediated Translational Control of Distinct mRNAs, Through Interaction With IRES cis-Regulatory Elements; 3.2. Posttranslational Modifications (PTMs) of RPs; 3.3. RP Gene Expression; 3.4. RP Paralogs; 3.5. Differential Subcellular Stoichiometry of RPs; 4. Exploring RP Roles in Health and Disease by Navigating Bioinformatics Resources; 4.1. RP Nomenclature
- 4.2. Comparison of Human and Mouse RPs Reveals a Range of Conservation Levels5. Conclusions and Future Directions; References; Chapter 4: Big Data, Artificial Intelligence, and Machine Learning in Neurotrauma; 1. Introduction; 2. Big Data: Characteristics, Definitions, and Examples; 3. Traumatic Brain Injury (TBI); 4. Big Data in TBI; 5. Machine Learning; 6. Artificial Intelligence; 7. Text Mining; 8. Examples of Using BD Approaches in TBI Research; 9. Imaging; 10. Biochemical Markers; 11. Legacy Data; 12. Future of Big Data in TBI; References
- Chapter 5: Artificial Intelligence Integration for Neurodegenerative Disorders1. Introduction; 2. Wearables and ML-Based Therapeutics; 3. Neurodegenerative Therapeutics Through AI; 3.1. Parkinson's Disease; 3.2. Seizures and Epilepsy; 3.3. Alzheimer's Disease; 3.4. Amyotrophic Lateral Sclerosis; 3.5. Stroke and Spinal Cord Injury; 4. AI-Based Clinical Decision-Making; 5. Limitations and Future Perspectives; References; Chapter 6: Robust Detection of Epilepsy Using Weighted-Permutation Entropy: Methods and Analysis; 1. Introduction; 1.1. Background; 1.2. Approach; 2. Computational Details