Predictive Big Data Analytics (PBDA)
This is SOCR and Big Data for Discovery Science public repository for code related to predictive big data analytics of brain aging, dementia, and motor tremor disorders.
The PBDA_PD folder contains the complete Predictive Big Data Analytics protocol (Pipeline workflow for obtaining a vector of hundreds of derived neuroimaging biomarkers and an R-script for machine-learning diagnostic classification and forecasting) that can be applied for Parkinson's Disease studies or any other clinical or translational biomedical investigation involving heterogeneous datasets.
Publications
- Dinov, ID, Heavner, B, Tang, M, Glusman, G, Chard, K, Darcy, M, Madduri, R, Pa, J, Spino, C, Kesselman, C, Foster, I, Deutsch, EW, Price, ND, Van Horn, JD, Ames, J, Clark, K, Hood, L, Hampstead, BM, Dauer, W, and Toga, AW. (2016) Predictive Big Data Analytics: A Study of Parkinson's Disease using Large, Complex, Heterogeneous, Incongruent, Multi-source and Incomplete Observations. PLoS ONE, 11(8):1-28, e0157077. DOI: 10.1371/journal.pone.0157077. PMID: 27494614.
- Gao, C, Sun, H, Wang, T, Tang, M, Bohnen, N, Muller, M, Herman, T, Giladi, N, Kalinin, AA, Spino, C, Dauer, W, Hausdorff, JM, Dinov, ID. (2018) Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease, Scientific Reports, DOI: 10.1038/s41598-018-24783-4.