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: Flavia Roberta Barbosa de Araujo
Institution: Federal University of Pernambuco
Department: Computer Science
Country:
Proposed Analysis: The purpose of this research is to apply and evaluate computational approaches based on machine learning when dealing with GWAS data. Throughout my work as a researcher, I have been studying different computational approaches capable of dealing with the inference of the interactions between SNPs [1] and [2]. More recently [3] and [4], I have been studying approaches based on machine learning specifically, algorithms based on Learning Vector Quantization (LVQ). In this research, I obtained interesting results only with the use of simulated data. Thus, with the objective of evaluating a new approach based on LVQ. I would like to have access to real data like GWAS data from Alzheimer's disease. Alzheimer's disease is one of great interest due to the damaging effects to the cerebral cortex. Identifying the risk factors associated with this disease can provide an early diagnosis, promoting a treatment or preventive measures of the occurrence of the disease. References: [1] Flavia Roberta Barbosa de Araujo; Katia Silva Guimaraes. Computational Tools for SNP Interactions - How Good Are They? The 2011 International Conference on Bioinformatics and Bioengineering. [2] Flavia Roberta Barbosa de Araujo; Eduardo Gade Gusmão; Katia Silva Guimarães. A Case-Control Study of Non-parametric Approaches for Detecting SNP-SNP Interactions. The 2011 International Conference of the Chilean Computer Science Society [3] Flavia R B Araujo; Hansenclever F Bassani; Aluizio F R Araujo. Learning vector quantization with local adaptive weighting for Genome-Wide association studies. The 2013 International Joint Conference on Neural Networks. [4] Flavia Roberta Barbosa De Araujo; Katia Silva Guimaraes. Inference of High-Order Epistatic Interactions Using Generalized Relevance Learning Vector Quantization with Parametric Adjustment. The 2016 International Conference on Tools with Artificial Intelligence.
Additional Investigators