Strategies in Biomedical Data Science: Driving Force for Innovation(Wiley and SAS Business Series)

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

About the Author :
JAY A. ETCHINGS is the director of operations at Arizona State University's 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.