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Big Data and Network Medicine in COPD

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COPD

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Abstract

Big Data provides substantial technical challenges related to organizing, moving, storing, and analyzing vast amounts of information. Importantly, useful “Big Data” needs to be more than just a lot of data; it needs to have complex, accurate, and relevant information. Key sources of Big Data in COPD research include genetics, other Omics (e.g., transcriptomics, proteomics, metabolomics, and epigenetics), and imaging. Network science provides approaches that can assist in the analysis of Big Data. Based on graph theory, networks provide a useful structure to visualize and analyze relationships—both linear and nonlinear—between variables of interest. Network Medicine involves the application of network science approaches to diseases. Key components of Network Medicine include identifying molecular interactions, defining optimal disease phenotypes, and integrating multiple Omics data. These three approaches are utilized to define disease-related networks, which will be utilized to reclassify diseases like COPD based on their etiology instead of end-stage physiological and pathological manifestations. Finally, new treatments and preventative strategies will be developed using systems pharmacology approaches. Integration of multiple types of Big Data will be essential in Network Medicine, and leveraging both Omics and animal model experimental data can overcome the limitations of an individual method. To realize the potential of these exciting opportunities in Big Data and Network Medicine, we will need to restructure the way that we study the pathogenesis of COPD and the approaches that we utilize to develop new COPD treatments.

This work was supported by NIH Grants R01 HL089856, P01 HL105339, R01 HL113264, P01 HL114501, and R01 HL111759 to EKS.

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Acknowledgements

The author thanks Dawn DeMeo, Craig Hersh, Peter Castaldi, and Michael Cho for helpful comments on this manuscript.

Conflict of Interest Statement In the past 3 years, Edwin K. Silverman received honoraria and consulting fees from Merck, grant support and consulting fees from GlaxoSmithKline, and honoraria from Novartis.

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Authors and Affiliations

  1. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA

    Edwin K. Silverman M.D., Ph.D.

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  1. Edwin K. Silverman M.D., Ph.D.
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  1. Department of Pulmonary and Critical, Asan Medical Center University of Ulsan College of Medicine, Seoul, Korea (Republic of)

    Sang-Do Lee

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Silverman, E.K. (2017). Big Data and Network Medicine in COPD. In: Lee, SD. (eds) COPD. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47178-4_22

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  • DOI: https://doi.org/10.1007/978-3-662-47178-4_22

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