There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Yalu Wen |
| Institution: | The University of Auckland |
| Department: | Statistics |
| Country: | |
| Proposed Analysis: | The goal of this project is to develop efficient analytical methods to integrate information from multiple sources to predict Alzheimer's Disease (AD). The specific aims are: 1) Use our recently developed kernel-fusion-based method to integrate multi-level data (e.g., genomic, gene expression and demographic variables) from ADNI study, where complex patterns in multi-level data are captured, and noise and redundant information are efficiently removed. 2) Use our recently developed kernel-fusion-based prediction model to predict AD-related phenotypes (e.g., hippocampus and ventricles), where genomic, gene expression, demographic variables are considered. 3) Develop a hierarchical prediction model to predict the progression of AD, where time-dependent information are integrated with the kernel-fusion method. |
| Additional Investigators |
