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Paper Details

Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.
PLoS One
54
2016
Alzheimer's, Huntington's, PD, PPMI, Parkinson's, Parkinson's Disease, Parkinson's disease, amyotrophic lateral sclerosis, chr12, human, neurodegenerative disorders, neurodegenerative processes, patient, rs34637584
Author NameAffiliation
Benjamin D HeavnerInstitute for Systems Biology
Gustavo GlusmanInstitute for Systems Biology
Mike D'ArcyInformation Sciences Institute, University of Southern California
Ravi MadduriComputation Institute, University of Chicago and Argonne National Laboratory
Ravi MadduriComputation Institute, University of Chicago and Argonne National Laboratory
Cathie SpinoUniversity of Michigan ann arbor
Carl KesselmanInformation Sciences Institute, University of Southern California
Ian FosterComputation Institute, University of Chicago and Argonne National Laboratory
Eric W DeutschInstitute for Systems Biology
Eric W DeutschInstitute for Systems Biology
Nathan D PriceInstitute for Systems Biology
Nathan D PriceInstitute for Systems Biology
John D Van HornStevens Neuroimaging and Informatics Institute, University of Southern California
John D Van HornStevens Neuroimaging and Informatics Institute, University of Southern California
Leroy HoodInstitute for Systems Biology
Leroy HoodInstitute for Systems Biology
William T DauerUniversity of Michigan ann arbor
Arthur W TogaStevens Neuroimaging and Informatics Institute, University of Southern California
Arthur W TogaStevens Neuroimaging and Informatics Institute, University of Southern California
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