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Strategies in Biomedical Data Science

Book Description

An essential guide to healthcare data problems, sources, and solutions

Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals.

Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution.

  • Consider the data challenges personalized medicine entails
  • Explore the available advanced analytic resources and tools
  • Learn how bioinformatics as a service is quickly becoming reality
  • Examine the future of IOT and the deluge of personal device data

The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

Table of Contents

  1. Foreword
  2. Acknowledgments
  3. Introduction
    1. WHO SHOULD READ THIS BOOK?
    2. WHAT’S IN THIS BOOK?
    3. HOW TO CONTACT US
  4. Chapter 1 Healthcare, History, and Heartbreak
    1. TOP ISSUES IN HEALTHCARE
    2. DATA MANAGEMENT
    3. BIOSIMILARS, DRUG PRICING, AND PHARMACEUTICAL COMPOUNDING
    4. PROMISING AREAS OF INNOVATION
    5. CONCLUSION
    6. NOTES
  5. Chapter 2 Genome Sequencing
    1. CHALLENGES OF GENOMIC ANALYSIS
    2. THE LANGUAGE OF LIFE
    3. A BRIEF HISTORY OF DNA SEQUENCING
    4. DNA SEQUENCING AND THE HUMAN GENOME PROJECT
    5. SELECT TOOLS FOR GENOMIC ANALYSIS
    6. The R Project
    7. Genome Analysis Toolkit
    8. Molecular Evolutionary Genetics Analysis
    9. Bowtie
    10. CONCLUSION
    11. Notes
    12. Note
  6. Chapter 3 Data Management
    1. BITS ABOUT DATA
    2. DATA TYPES
    3. DATA SECURITY AND COMPLIANCE
    4. DATA STORAGE
    5. SWIFTSTACK
    6. CONCLUSION
    7. NOTES
    8. Note
  7. Chapter 4 Designing a Data-Ready Network Infrastructure
    1. RESEARCH NETWORKS: A PRIMER
    2. ESNET AT 30: EVOLVING TOWARD EXASCALE AND RAISING EXPECTATIONS
    3. INTERNET2 INNOVATION PLATFORM
    4. ADVANCES IN NETWORKING
    5. INFINIBAND AND MICROSECOND LATENCY
    6. THE FUTURE OF HIGH-PERFORMANCE FABRICS
    7. NETWORK FUNCTION VIRTUALIZATION
    8. SOFTWARE-DEFINED NETWORKING
    9. OPENDAYLIGHT
    10. CONCLUSION
    11. NOTES
  8. Chapter 5 Data-Intensive Compute Infrastructures
    1. BIG DATA APPLICATIONS IN HEALTH INFORMATICS
    2. SOURCES OF BIG DATA IN HEALTH INFORMATICS
    3. INFRASTRUCTURE FOR BIG DATA ANALYTICS
    4. FUNDAMENTAL SYSTEM PROPERTIES
    5. GPU-ACCELERATED COMPUTING AND BIOMEDICAL INFORMATICS
    6. CONCLUSION
    7. NOTES
    8. NOTES
    9. INTRODUCTION
    10. EVIS
    11. SCIENTIFIC COMPUTING
    12. VALIDATION
    13. MEDICAL DEVICE DEVELOPMENT
    14. CONCLUSION
    15. Note
  9. Chapter 6 Cloud Computing and Emerging Architectures
    1. CLOUD BASICS
    2. CHALLENGES FACING CLOUD COMPUTING APPLICATIONS IN BIOMEDICINE
    3. HYBRID CAMPUS CLOUDS
    4. RESEARCH AS A SERVICE
    5. FEDERATED ACCESS WEB PORTALS
    6. CLUSTER HOMOGENEITY
    7. EMERGING ARCHITECTURES (ZETA ARCHITECTURE)
    8. CONCLUSION
    9. NOTES
  10. Chapter 7 Data Science
    1. NOSQL APPROACHES TO BIOMEDICAL DATA SCIENCE
    2. USING SPLUNK FOR DATA ANALYTICS
    3. STATISTICAL ANALYSIS OF GENOMIC DATA WITH HADOOP
    4. EXTRACTING AND TRANSFORMING GENOMIC DATA
    5. PROCESSING EQTL DATA
    6. GENERATING MASTER SNP FILES FOR CASES AND CONTROLS
    7. GENERATING GENE EXPRESSION FILES FOR CASES AND CONTROLS
    8. CLEANING RAW DATA USING MAPREDUCE
    9. TRANSPOSE DATA USING PYTHON
    10. STATISTICAL ANALYSIS USING SPARK
    11. HIVE TABLES WITH PARTITIONS
    12. CONCLUSION
    13. NOTES
    14. Appendix: A Brief Statistics PrimerContent Contributed by Daniel Peñnaherrera,July 13, 2016
    15. FOUNDATIONS
    16. POPULATION AND SAMPLE
    17. RANDOM VARIABLES
    18. EXPECTED VALUE AND VARIANCE
    19. REGRESSION ANALYSIS
    20. MULTIVARIATE LINEAR REGRESSION
    21. LOGISTIC REGRESSION
  11. Chapter 8 Next-Generation Cyberinfrastructures
    1. Next-Generation Cyber Capability
    2. NGCC DESIGN AND INFRASTRUCTURE
    3. Conclusion
    4. NOTE
  12. Conclusion
  13. Appendix A The Research Data Management Survey
  14. Appendix B Central IT and Research Support
    1. INSTITUTIONAL DEMOGRAPHICS (BACKGROUND)
    2. OVERVIEW OF CENTRAL IT ORGANIZATIONS
    3. CENTRAL IT INFRASTRUCTURE
    4. CENTRAL IT RESEARCH SUPPORT SERVICES
    5. CENTRAL IT OFFERED SERVICES
    6. FUNDING MECHANISMS
    7. SUMMARY AND CONCLUSIONS
    8. REFERENCES
  15. Appendix C HPC Working Example
  16. Appendix D HPC and Hadoop
  17. Appendix E Bioinformatics + Docker
  18. Glossary
  19. About the Author
  20. About the Contributors
  21. Index
  22. EULA