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: | Wenhai Cui |
| Institution: | Shandong University |
| Department: | Zhongtai Securities Institute for Financial Study |
| Country: | |
| Proposed Analysis: | Ensemble learning that aggregates multiple diverse models is an effective method for making reliable predictions. However, it is noticed that the conventional methods are designed for complete observations. In practice, we often confront blockwise missing data gathered from different sources, modalities, or surveys, which brings enormous challenges to imputation and prediction. In this paper, we propose a sequential ensemble-based method for a high-dimensional linear regression model with block-wise missing covariates in the general semi-supervised framework. By leveraging the structure of missing patterns, we propose a computationally efficient semi-supervised estimator based on multiple-blockwise imputations and sequential ensemble-based procedures. Importantly, unlike the existing methods, we do not impose on requiring any complete observations across the available samples. We will use data from ADNI to identify the various biomarkers that are associated with the score of the mini-mental state examination. |
| Additional Investigators |
