Thanks to the development of modern data collection and analytic techniques, biomedical research generates increasingly large amounts of data in various formats and at all levels, which is referred to as big data. Big data is a collection of data sets, which are large in volume and complex in structure. To illustrate, the data managed by America’s leading healthcare provider Kaiser is 4,000 times more than the amount of information stored in the Library of Congress. As to data structure, the range of nutritional data types and sources make it really difficult to normalize. Such volume and complexity of big data make it difficult to be processed by traditional data analytic techniques. Therefore, to further knowledge and uncover hidden value, there is an increasing need to better understand and mine biomedical big data by innovative techniques and new approaches, which requires interdisciplinary collaborations involving data providers and users (e.g., biomedical researchers,...
- Institute of Medicine. (2009). Beyond the HIPAA privacy rule: Enhancing privacy, improving health through research. Washington, DC: The National Academies Press.Google Scholar
- LeDuc, R., Vaughn, M., Fonner, J. M., Sullivan, M., Williams, J. G., Blood, P. D., et al. (2014). Leveraging the national cyberinfrastructure for biomedical research. Journal of the American Medical Informatics Association, 21(2), 195–199.Google Scholar
- Margolis, R., Derr, L., Dunn, M., Huerta, M., Larkin, J., Sheehan, J., et al. (2014). The National Institutes of Health’s Big Data to Knowledge (BD2K) initiative: Capitalizing on biomedical big data. Journal of the American Medical Informatics Association, 21(6), 957–958.Google Scholar
- Weber, G., Mandl, K. D., & Kohane, I. S. (2014). Finding the missing link for big biomedical data. Journal of American Medical Association, 331(4), 2479-2480.Google Scholar