Big Data Analytics in Bioinformatics: Microservices Architecture in the Cloud - Alzheimer's Surveillance Re-envisioned Open Access
Rivera, Brooke (Fall 2017)
Research indicates that over eighty percent of brain disorders are associated with genomics defects in conjunction with environmental factors and epigenetic phenomena. There is a significant body of evidence that suggests that the confluence of next-generation sequencing (NGS), cloud computing, and advanced analytics are producing a paradigm shift in our understanding of disease etiology, development and treatment, challenging the traditional surveillance model. Advances in computing power and “big data” provide the engine for mathematical and statistical techniques by which disparate datasets can be synthesized and analyzed. Whole-genome sequencing (WGS) offers precision resolution in orders of magnitude over earlier genotyping methods, transforming our approach to monitor, control, and prevent diseases in both epidemic and endemic contexts. In addition, the increased availability of dense data characterizing the substrate population and the development of advanced computation and analytical tools to organize and interpret these large datasets broadens the potential for application of such data to high-resolution epidemiological problems. In order to harness this potential, global surveillance systems and initiatives must be strengthened, tightly coordinated, and judiciously harmonized to inform global strategies, monitor the effectiveness of public health interventions, and detect new threats. Esperanza 3.0, an integrated cloud-based multi-disciplinary surveillance platform, will offer the foundation for this united global response.
Esperanza 3.0 is envisioned to be a highly secure containerized microservices platform which will host an international repository for Alzheimer’s case notifications, electronic lab reports, and imaging records for participating nation-states. Cloud computing offers unprecedented access to highly performant, supercomputing resources historically reserved for military and classified research facility operations – allowing us to leverage the scalability, elasticity, enriched analytics capacity, and computing power. Deep drilling and cluster analysis will be introduced for longitudinal research studies, monitoring, control and intervention. This platform will be architected to modernize, optimize, and ultimately revolutionize chronic disease surveillance by integrating modern data visualization and advanced analytics.
Table of Contents
N/A - Not Applicable
About this Master's Thesis
|Subfield / Discipline|
|Committee Chair / Thesis Advisor|
|Big Data Analytics in Bioinformatics: Microservices Architecture in the Cloud - Alzheimer's Surveillance Re-envisioned ()||2017-11-30 19:42:13 -0500||