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