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Strategies in Biomedical Data Science: Driving Force for Innovation [Kietas viršelis]

  • Formatas: Hardback, 464 pages, aukštis x plotis x storis: 257x183x46 mm, weight: 885 g
  • Serija: Wiley and SAS Business Series
  • Išleidimo metai: 07-Mar-2017
  • Leidėjas: John Wiley & Sons Inc
  • ISBN-10: 1119232198
  • ISBN-13: 9781119232193
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 464 pages, aukštis x plotis x storis: 257x183x46 mm, weight: 885 g
  • Serija: Wiley and SAS Business Series
  • Išleidimo metai: 07-Mar-2017
  • Leidėjas: John Wiley & Sons Inc
  • ISBN-10: 1119232198
  • ISBN-13: 9781119232193
Kitos knygos pagal šią temą:
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.

Foreword xi
Acknowledgments xv
Introduction
1(2)
Who Should Read This Book?
3(1)
What's in This Book?
4(2)
How to Contact Us
6(1)
Chapter 1 Healthcare, History, and Heartbreak
7(20)
Top Issues in Healthcare
9(7)
Data Management
16(2)
Biosimilars, Drug Pricing, and Pharmaceutical Compounding
18(1)
Promising Areas of Innovation
19(6)
Conclusion
25(1)
Notes
25(2)
Chapter 2 Genome Sequencing: Know Thyself, One Base Pair at a Time
27(26)
Sheetal Shetty
Jacob Brill
Challenges of Genomic Analysis
29(1)
The Language of Life
30(1)
A Brief History of DNA Sequencing
31(4)
DNA Sequencing and the Human Genome Project
35(3)
Select Tools for Genomic Analysis
38(9)
Conclusion
47(1)
Notes
48(5)
Chapter 3 Data Management
53(52)
Joe Arnold
Bits about Data
54(2)
Data Types
56(3)
Data Security and Compliance
59(7)
Data Storage
66(4)
SwiftStack
70(8)
OpenStack Swift Architecture
78(16)
Conclusion
94(1)
Notes
94(11)
Chapter 4 Designing a Data-Ready Network Infrastructure
105(58)
Research Networks: A Primer
108(1)
ESnet at 30: Evolving toward Exascale and Raising Expectations
109(2)
Internet2 Innovation Platform
111(2)
Advances in Networking
113(1)
InfiniBand and Microsecond Latency
114(3)
The Future of High-Performance Fabrics
117(2)
Network Function Virtualization
119(2)
Software-Defined Networking
121(1)
OpenDaylight
122(35)
Conclusion
157(1)
Notes
157(6)
Chapter 5 Data-Intensive Compute Infrastructures
163(48)
Dijiang Huang
Yuli Deng
Jay Etchings
Zhiyuan Ma
Guangchun Luo
Big Data Applications in Health Informatics
166(2)
Sources of Big Data in Health Informatics
168(3)
Infrastructure for Big Data Analytics
171(15)
Fundamental System Properties
186(1)
GPU-Accelerated Computing and Biomedical Informatics
187(3)
Conclusion
190(1)
Notes
191(20)
Chapter 6 Cloud Computing and Emerging Architectures
211(24)
Cloud Basics
213(2)
Challenges Facing Cloud Computing Applications in Biomedicine
215(1)
Hybrid Campus Clouds
216(1)
Research as a Service
217(2)
Federated Access Web Portals
219(1)
Cluster Homogeneity
220(1)
Emerging Architectures (Zeta Architecture)
221(8)
Conclusion
229(1)
Notes
229(6)
Chapter 7 Data Science
235(72)
NoSQL Approaches to Biomedical Data Science
237(7)
Using Splunk for Data Analytics
244(6)
Statistical Analysis of Genomic Data with Hadoop
250(3)
Extracting and Transforming Genomic Data
253(3)
Processing eQTL Data
256(3)
Generating Master SNP Files for Cases and Controls
259(1)
Generating Gene Expression Files for Cases and Controls
260(1)
Cleaning Raw Data Using MapReduce
261(2)
Transpose Data Using Python
263(1)
Statistical Analysis Using Spark
264(4)
Hive Tables with Partitions
268(2)
Conclusion
270(1)
Notes
270(20)
Appendix: A Brief Statistics Primer
290(17)
Daniel Penaherrera
Chapter 8 Next-Generation Cyberinfrastructures
307(30)
Next-Generation Cyber Capability
308(2)
NGCC Design and Infrastructure
310(17)
Conclusion
327(3)
Note
330(5)
Conclusion
335(2)
Appendix A The Research Data Management Survey: From Concepts to Practice 337(16)
Brandon Mikkelsen
Jay Etchings
Appendix B Central IT and Research Support 353(24)
Gregory D. Palmer
Appendix C HPC Working Example: Using Parallelization Programs Such as GNU Parallel and OpenMP with Serial Tools 377(8)
Appendix D HPC and Hadoop: Bridging HPC to Hadoop 385(6)
Appendix E Bioinformatics + Docker: Simplifying Bioinformatics Tools Delivery with Docker Containers 391(8)
Glossary 399(20)
About the Author 419(2)
About the Contributors 421(6)
Index 427
JAY A. ETCHINGS is the director of operations at Arizona State Universitys Research Computing program, where he is responsible for developing innovative architectures to progress fluid technical environments supporting highly computational workloads, peta-scale data analysis, next-generation cyber capabilities, and emerging network innovations.