Inside Advanced Scale Challenges|Monday, November 20, 2017
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‘Strategies in Biomedical Data Science’ Advances IT-Research Synergies 

“Strategies in Biomedical Data Science: Driving Force for Innovation” by Jay A. Etchings (John Wiley & Sons, Inc., Jan. 2017) is both an introductory text and a field guide for anyone working with biomedical data, IT professionals as well as medical and research staff.

Director of operations at Arizona State University’s Research Computing program, Etchings writes that the primary motivation for the book was to bridge the divide “between IT and data technologists, on one hand, and the community of clinicians, researchers, and academics who deliver and advance healthcare, on the other.” As biology and medicine move squarely into the realm of data sciences, driven by the twin engines of big compute and big data, removing the traditional silos between IT and biomedicine will allow both groups to work better and more efficiently, Etchings asserts.

“Work in sciences is routinely compartmentalized and segregated among specialists,” ASU Professor Ken Buetow, PhD, observes in the foreword. “This segregation is particularly true in biomedicine as it wrestles with the integration of data science and its underpinnings in information technology. While such specialization is essential for progress within disciplines, the failure to have cross-cutting discussions results in lost opportunities.”

Aimed at this broader audience, “Strategies in Biomedical Data Science” introduces readers to the cutting-edge and fast moving field of biomedical data. The 443-page book lays out a foundation in the concepts in data management biomedical sciences and empowers readers to:

Efficiently gather data from disparate sources for effective analysis;

Get the most out of the latest and preferred analytic resources and technical tool sets; and

Intelligently examine bioinformatics as a service, including the untapped possibilities for medical and personal health devices.

A diverse array of use cases and case studies highlight specific applications and technologies being employed to solve real-world challenges and improve patient outcomes. Contributing authors, experts working and studying at the intersection of IT and biomedicine, offer their knowledge and experience in traversing this rapidly-changing field.

Arizona State University’s director of operations for research computing and senior HPC architect Jay Etchings

We reached out to BioTeam VP Ari Berman to get his view on the IT/research gap challenge. “This is exactly what BioTeam [a life sciences computing consultancy] is focused on,” he told us. “Since IT organizations have traditionally supported business administration needs, they are not always equipped to handle the large amounts of data that needs to be moved and stored, or the amount of computational power needed to run the analysis pipelines that may yield new discoveries for the scientists. Because of this infrastructure, skills, and services gap between IT and biomedical data science, many research organizations spend too much time and money trying to bridge that gap on their own through cloud infrastructures or shadow IT running in their laboratories. I’ve spent my career bridging this gap, and I can tell you first hand that doing it correctly has certainly moved the needle forward on scientists’ ability to make new discoveries.”

Never lost in this far-ranging survey of biomedical data challenges and strategies is the essential goal: to improve human life and reduce suffering. Etchings writes that the book was inspired by “the need for a collaborative and multidisciplinary approach to solving the intricate puzzle that is cancer.” Author proceeds support the Pediatric Brain Tumor Foundation. The charity serves the more than 28,000 children and teens in the United States who are living with the diagnosis of a brain tumor.

To read an excerpt, visit the book page on the publisher’s website.

A listing of Chapter headings:

Chapter 1 Healthcare, History, and Heartbreak 7

Chapter 2 Genome Sequencing: Know Thyself, One Base Pair at a Time 27

Chapter 3 Data Management 53

Chapter 4 Designing a Data-Ready Network Infrastructure 105

Chapter 5 Data-Intensive Compute Infrastructures 163

Chapter 6 Cloud Computing and Emerging Architectures 211

Chapter 7 Data Science 235

Chapter 8 Next-Generation Cyberinfrastructures 307

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