ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Sara Madeira |
| Institution: | INESC-ID Lisbon |
| Department: | KDBIO group |
| Country: | |
| Proposed Analysis: | Study state of the art results of Genome Wide Association Studies applied to neurodegenerative diseases and review some of the most important studies and genes already discovered to be involved. Then, we plan to apply and propose new data mining approaches that can filter important SNPs and predict the prognosis for each of the diseases in study with an accuracy as high as possible. This analysis will be as follows: (1) search for SNPs that are highly associated with the disease; (2) search for SNPs that are not associated with the disease when considered alone, but that are associated with the disease when considered together with other SNPs; (3) compare results of the two approaches just described, each one considered alone and an hybrid approach that considers SNPs of the two kinds described; (4) compare the results with studies performed over the same datasets. The results must be analyzed in terms of the accuracy of the prediction, SNPs discovered and validate them with knowledge already existing on the literature or propose new associations. The association analysis described above will rely on existing networks that describe interactions such as STRING. |
| Additional Investigators | |
| Investigator's Name: | Orlando Anunciação |
| Proposed Analysis: | Study and develop new data mining techniques for the diagnosis and prognosis of neurodegenerative diseases by analyzing omics data. These techniques should be able to extract relevant features to disease prognostic and identification of SNPs and genes that may be involved in disease development. In particular, the discovery of relations between genetic markers will be tackled. The putative relations identified should be validated using existing knowledge on gene-gene and gene-disease interactions. |