Big Data Analytics in Bioinformatics: Microservices Architecture in the Cloud - Alzheimer's Surveillance Re-envisioned Pubblico

Abstract

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

Rights statement

• Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Rollins School of Public Health
Department	Executive Masters of Public Health - MPH
Subfield / Discipline	Applied Public Health Informatics
Degree	M.P.H.
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Public Health
Parola chiave	Dementia Enterprise Architecture Bioinformatics Surveillance Microservices Alzheimer's Informatics Cloud Computing Big Data
Committee Chair / Thesis Advisor	Gaydos, Laurie, Emory University Alperin, Moose, Emory University
Partnering Agencies	Does not apply (no collaborating organization)

Ultima modifica

Primary PDF

Thumbnail	Title	Date Uploaded	Actions
	Big Data Analytics in Bioinformatics: Microservices Architecture in the Cloud - Alzheimer's Surveillance Re-envisioned ()	2017-11-30 19:42:13 -0500	Press to Select an action Download

Supplemental Files

Thumbnail	Title	Date Uploaded	Actions