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: | Valentina Escott-Price |
| Institution: | Cardiff University |
| Department: | Neuroscience and Mental Health |
| Country: | |
| Proposed Analysis: | We aim to investigate supervised and unsupervised machine learning (ML) approaches for prediction of AD risk with genetic, and biomarkers variables. We will compare the prediction accuracy of ML with statistical approaches using the polygenic risk scores as predictors. We aim to replicate the results in AIBL and ADNI-DOD. |
| Additional Investigators | |
| Investigator's Name: | Eftychia Bellou |
| Proposed Analysis: | We aim to investigate supervised and unsupervised machine learning (ML) approaches for prediction of AD risk with genetic, and biomarkers variables. We will compare the prediction accuracy of ML with statistical approaches using the polygenic risk scores as predictors |
| Investigator's Name: | Emily Baker |
| Proposed Analysis: | We aim to investigate supervised and unsupervised machine learning (ML) approaches for prediction of AD risk with genetic, and biomarkers variables. We will compare the prediction accuracy of ML with statistical approaches using the polygenic risk scores as predictors. We aim to replicate the results in AIBL and ADNI-DOD. |
| Investigator's Name: | Ganna Leonenko |
| Proposed Analysis: | We aim to investigate supervised and unsupervised machine learning (ML) approaches for prediction of AD risk with genetic, and biomarkers variables. We will compare the prediction accuracy of ML with statistical approaches using the polygenic risk scores as predictors. We aim to replicate the results in AIBL and ADNI-DOD. |
