Ongoing Investigations

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: Yesim AYDIN SON
Institution: Middle East technical University
Department: Health Informatics
Country:
Proposed Analysis: I request access to the ADNI data in order to participate in the Alzheimer’s Disease Big Data DREAM Challenge #1. The goal of this Challenge is to use a crowd-based competition framework to develop validated molecular predictors of cognitive decline in Alzheimer's disease and predictors of discordance between amyloid perturbation and cognitive function. If successful the results of the study is intended to be used to design a pilot project for Turkish patient cohort, where all results will be published publicly.
Additional Investigators  
Investigator's Name: Onur Erdogan
Proposed Analysis: I request access to the ADNI data in order to participate in the Alzheimer’s Disease Big Data DREAM Challenge #1. The goal of this Challenge is to use a crowd-based competition framework to develop validated molecular predictors of cognitive decline in Alzheimer's disease and predictors of discordance between amyloid perturbation and cognitive function.
  
Investigator's Name: Onur Erdoğan
Proposed Analysis: Main goal of my research towards my doctoral degree, is to develop a predictive AD model with the highest classification performance. We will perform the meta-analysis of different AD data sets (ADNI, dbGAP, in-house…) separately, which have been obtained independently so far, and apply different types of hybrid data mining approaches for both the preprocessing step and the model construction. After statistical meta-analysis of each dataset, Analytical Hierarchy Process (AHP) or Random Forest (RF) technique will be implemented. Different combinations among the avaiable data-mining approaches will be used to determine the hybrid decision model with performance. Belief networks methods and other emerging apporaches will Be utilized for finalizing the meta-model, based on the large scale genotype-phenotype association data. Uncovering the genetic basis for disease is the critical step toward the goal of developing an effective system of “personalized” medicine.