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: | Paul Martino |
| Institution: | Houghton College |
| Department: | Chemistry |
| Country: | |
| Proposed Analysis: | We intend to analyze ADNI data to identify small molecules for further testing in Alzheimer's Disease treatment and prevention. We intend to mine patient data that can be linked to small molecules such as medical and drug histories, proteomics data and additional genetic biomarkers. We hope to discover any information related to the reduction of Alzheimer's disease. We will use a number of Feature Selections techniques and statistical assessments to evaluate which patient variables are associated with AD. The statistical analyses we will use include but are not limited to, Pearson’s R, Fisher Score, T-tests, Chi Squared tests, Lasso Logistic / Linear regression, and Stepwise/Recursive Logistic/Linear regression. A second screening will then be conducted through the use of Autodock Vina 1.2 while considering the amyloid cascade hypothesis. This will test the ability of identified small molecules to inhibit the aggregation of Amyloid-beta(1-42). A subset of these top scoring molecules will then be given to the biochemistry team for in vitro studies and in vivo with C. elegans AD models. |
| Additional Investigators | |
| Investigator's Name: | Liam Fisher |
| Proposed Analysis: | We intend to analyze ADNI data to identify small molecules for further testing in Alzheimer's Disease treatment and prevention. We intend to mine patient data that can be linked to small molecules such as medical and drug histories, proteomics data and additional genetic biomarkers. We hope to discover any information related to the reduction of Alzheimer's disease. We will use a number of Feature Selections techniques and statistical assessments to evaluate which patient variables are associated with AD. The statistical analyses we will use include but are not limited to, Pearson’s R, Fisher Score, T-tests, Chi Squared tests, Lasso Logistic / Linear regression, and Stepwise/Recursive Logistic/Linear regression. A second screening will then be conducted through the use of Autodock Vina 1.2 while considering the amyloid cascade hypothesis. This will test the ability of identified small molecules to inhibit the aggregation of Amyloid-beta(1-42). A subset of these top scoring molecules will then be given to the biochemistry team for in vitro studies and in vivo with C. elegans AD models. |
| Investigator's Name: | Gabriella Mancini |
| Proposed Analysis: | We intend to analyze ADNI data to identify small molecules for further testing in Alzheimer's Disease treatment and prevention. We intend to mine patient data that can be linked to small molecules such as medical and drug histories, proteomics data and additional genetic biomarkers. We hope to discover any information related to the reduction of Alzheimer's disease. We will use a number of Feature Selections techniques and statistical assessments to evaluate which patient variables are associated with AD. The statistical analyses we will use include but are not limited to, Pearson’s R, Fisher Score, T-tests, Chi Squared tests, Lasso Logistic / Linear regression, and Stepwise/Recursive Logistic/Linear regression. A second screening will then be conducted through the use of Autodock Vina 1.2 while considering the amyloid cascade hypothesis. This will test the ability of identified small molecules to inhibit the aggregation of Amyloid-beta(1-42). A subset of these top scoring molecules will then be given to the biochemistry team for in vitro studies and in vivo with C. elegans AD models. |
| Investigator's Name: | Gabriella Mancini |
| Proposed Analysis: | We intend to analyze ADNI data to identify small molecules for further testing in Alzheimer's Disease treatment and prevention. We intend to mine patient data that can be linked to small molecules such as medical and drug histories, proteomics data and additional genetic biomarkers. We hope to discover any information related to the reduction of Alzheimer's disease. We will use a number of Feature Selections techniques and statistical assessments to evaluate which patient variables are associated with AD. The statistical analyses we will use include but are not limited to, Pearson’s R, Fisher Score, T-tests, Chi Squared tests, Lasso Logistic / Linear regression, and Stepwise/Recursive Logistic/Linear regression. A second screening will then be conducted through the use of Autodock Vina 1.2 while considering the amyloid cascade hypothesis. This will test the ability of identified small molecules to inhibit the aggregation of Amyloid-beta(1-42). A subset of these top scoring molecules will then be given to the biochemistry team for in vitro studies and in vivo with C. elegans AD models. |
