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Activity Number: 311 - Modern Statistical Methods for Imaging Genomics
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Imaging
Abstract #313396
Title: Thresholded Graph Laplacian Gaussian Priors for Bayesian Network Marker Selection with Application to Cancer Imaging Genomics
Author(s): Jian Kang*
Companies: University of Michigan
Keywords: Bayesian methods; Graph Laplacian; posterior consistency

Selecting network markers becomes increasingly important in cancer imaging genomics. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this talk, I will introduce a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of imaging genomics dataset in the Cancer Genome Atlas (TCGA).

Authors who are presenting talks have a * after their name.

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