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: | Caleb Ellington |
| Institution: | Carnegie Mellon University |
| Department: | Computational Biology Department |
| Country: | |
| Proposed Analysis: | The heterogeneity of AD hinders traditional modeling approaches that require many statistical samples. We propose to use contextual modeling (a machine learning paradigm) to infer patient-specific models of AD state and progression in the context of patient genomics, imaging, clinical measurements. To infer AD state, we will estimate contextual transcriptomic regulatory networks, representing the RNA expression dynamics under clinical, imaging, and genomic contexts. Similarly, we propose to infer models for AD prognosis by estimating the context-specific effects of clinical measurements on AD outcomes. We plan to apply our contextual modeling methods (https://contextualized.ml/) to infer novel model-based subtypes of AD state and progression that improve current AD subtypes. |
| Additional Investigators | |
| Investigator's Name: | Alyssa Lee |
| Proposed Analysis: | The heterogeneity of AD hinders traditional modeling approaches that require many statistical samples. We propose to use contextual modeling (a machine learning paradigm) to infer patient-specific models of AD state and progression in the context of patient genomics, imaging, clinical measurements. To infer AD state, we will estimate contextual transcriptomic regulatory networks, representing the RNA expression dynamics under clinical, imaging, and genomic contexts. Similarly, we propose to infer models for AD prognosis by estimating the context-specific effects of clinical measurements on AD outcomes. We plan to apply our contextual modeling methods (https://contextualized.ml/) to infer novel model-based subtypes of AD state and progression that improve current AD subtypes. |
