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Activity Number: 72
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #315855 View Presentation
Title: A Bayesian Predictive Model for Imaging Genetics with Application to Schizophrenia
Author(s): Thierry Chekouo * and Francesco Stingo and Michele Guindani and Kim-Anh Do
Companies: MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
Keywords: Imaging genetics ; fMRI ; Bayesian variable selection ; Markov random field ; non local prior
Abstract:

In this talk, I'll present an integrative Bayesian risk prediction model that allows to discriminate between schizophrenic patients and healthy controls, based on a sparse set of discriminatory ROIs and SNPs. Inference on a regulatory network between SNPs and ROI intensities (ROI-SNP network) is used in a single modeling framework to inform the selection of the discriminatory ROIs and SNPs. Using simulation studies, we assess the performance of our method both in terms of prediction and variable selection, and we apply it to a schizophrenia data. We confirm that some biomarkers involved in the ROI-SNP network are more likely to be discriminating.


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