The goal of this project is to develop, validate and apply novel computational and system biology algorithms and tools for comprehensive joint analysis of large-scale heterogeneous imaging genomic data, with applications to early prediction of conversion of Mild Cognitive Impairment (MCI) to Alzheimer Disease (AD). UNC team will work on Aims 1 and 2 of this project, for developing a novel sparse learning based system biology framework for analysis of genome-wide association results across a large number of the structural and functional phenotypes derived from MRI and PET scans of the whole brain, as well as for developing the new structured sparsity-inducing norms based multi-dimensional data integration methods to identify the biomarkers from heterogeneous imaging genomic data for MCI conversion prediction.
|Effective start/end date||9/1/15 → 4/30/20|
- University of Texas at Arlington
Magnetic resonance imaging