Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

Article English OPEN
Dinov, Ivo D.; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W.; Price, Nathan D.; Van Horn, John D.; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M.; Dauer, William; Toga, Arthur W.;
(2016)
  • Publisher: Public Library of Science (PLoS)
  • Journal: PLoS ONE,volume 11,issue 8 (issn: 1932-6203, eissn: 1932-6203)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.1371/journal.pone.0157077, pmc: PMC4975403
  • Subject: Research Article | Diagnostic Medicine | Mathematics | Mathematical and Statistical Techniques | Artificial Intelligence | Neurodegenerative Diseases | Physical Sciences | People and Places | Demography | Neurology | Statistics (Mathematics) | Biology and Life Sciences | Computer and Information Sciences | Forecasting | Neuroscience | Biomarkers | Research and Analysis Methods | Medicine | Parkinson Disease | Machine Learning | Neuroimaging | Q | R | Imaging Techniques | Statistical Data | Biochemistry | Science | Medicine and Health Sciences | Movement Disorders | Statistical Methods | Cognitive Science

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 opportuni... View more