Leveraging Biomedical and Healthcare Data
eBook - ePub

Leveraging Biomedical and Healthcare Data

Semantics, Analytics and Knowledge

  1. 225 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Leveraging Biomedical and Healthcare Data

Semantics, Analytics and Knowledge

About this book

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.- Includes at each section resource pages containing a list of available curated raw and processed data that can be used by researchers in the field- Provides demonstrative and relevant examples that serve as a general tutorial- Presents a list of algorithm names and computational tools available for basic and clinical researchers

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Yes, you can access Leveraging Biomedical and Healthcare Data by Firas Kobeissy,Kevin Wang,Fadi A. Zaraket,Ali Alawieh in PDF and/or ePUB format, as well as other popular books in Medicine & Biostatistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Academic Press
Year
2018
Print ISBN
9780128095560
eBook ISBN
9780128095614
Topic
Medicine
Subtopic
Biostatistics

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Contributors
  7. Foreword I
  8. Foreword II
  9. Preface
  10. Acknowledgments
  11. Chapter 1: Comprehensive Workflow for Integrative Transcriptomics Meta-Analysis
  12. Chapter 2: Proteomics and Protein Interaction in Molecular Cell Signaling Pathways
  13. Chapter 3: Understanding Specialized Ribosomal Protein Functions and Associated Ribosomopathies by Navigating Across Sequence, Literature, and Phenotype Information Resources
  14. Chapter 4: Big Data, Artificial Intelligence, and Machine Learning in Neurotrauma
  15. Chapter 5: Artificial Intelligence Integration for Neurodegenerative Disorders
  16. Chapter 6: Robust Detection of Epilepsy Using Weighted-Permutation Entropy: Methods and Analysis
  17. Chapter 7: Biological Knowledge Graph Construction, Search, and Navigation
  18. Chapter 8: Healthcare Decision-Making Support Based on the Application of Big Data to Electronic Medical Records: A Knowledge Management Cycle
  19. Chapter 9: Computational Modeling in Global Infectious Disease Epidemiology
  20. Chapter 10: Semiautomatic Annotator for Medical NLP Applications: About the Tool
  21. Chapter 11: Intractome Curation and Analysis for Stroke and Spinal Cord Injury Using Semiautomatic Annotations
  22. Chapter 12: Deep Genomics and Proteomics: Language Model-Based Embedding of Biological Sequences and Their Applications in Bioinformatics
  23. Chapter 13: In Silico Transcription Factor Discovery via Bioinformatics Approach: Application on iPSC Reprogramming Resistant Genes
  24. Index