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: | Stephen Yi |
| Institution: | University of Texas at Austin |
| Department: | Dell Medical School |
| Country: | |
| Proposed Analysis: | Heterogeneity and complex biological factors in Alzheimer's disease (AD) make prognosis prediction challenging. Treatment strategies in high-risk AD are still limited. Therefore, effective computational methods to distinguish high-risk from low-risk AD will be promising in the disease diagnosis and prognosis. By efficiently integrating various data types available at ADNI, we aim to gain new insights regarding the possible mechanism of how AD develops and progresses. We will treat each type of data such as genetic and clinical profiles as individual matrices and take the integrated matrices as input in our designed autoencoder for dimensional reduction. We then will select informative features after Cox-PH model to classify patients into high-risk and low-risk subgroups. Next, we will build and optimize subgrouping prediction models from the combination of potential data signatures or single-omics data. Furthermore, we will employ some feature extraction algorithms to the prediction models and identify potential therapeutic signatures that are putative drug targets. |
| Additional Investigators |
