Abstract

Background
A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.
Methods and Findings
Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for …

Date

August 5, 2016

Authors

Ivo D Dinov, Ben Heavner, Ming Tang, Gustavo Glusman, Kyle Chard, Mike Darcy, Ravi Madduri, Judy Pa, Cathie Spino, Carl Kesselman, Ian Foster, Eric W Deutsch, Nathan D Price, John D Van Horn, Joseph Ames, Kristi Clark, Leroy Hood, Benjamin M Hampstead, William Dauer, Arthur W Toga

Journal

PloS one

Volume

11

Issue

8

Pages

e0157077

Publisher

Public Library of Science