Neural Network Feature Extraction and Bayesian Group Sparse Multitask Regression for Imaging Genetics
Farouk Nathoo, PhD
Professor, Mathematics and Statistics
University of Victoria, BC
WHEN: Wednesday, February 21, 2024, from 3:30 to 4:30 p.m.
WHERE: hybrid | 2001 91社区 College Avenue, room 1201;
NOTE: Dr. Nathoo will be presenting from Victoria, BC
Abstract
Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer鈥檚 Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes.
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