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Activity Number: 343 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #323278
Title: Bayesian Predictive Modeling from Multi-Source Multiway Data
Author(s): Jonathan Kim* and Eric F Lock
Companies: University of Minnesota and University of Minnesota
Keywords: Bayesian; High-Dimensional; Omics; Multi-source; Multi-way

We develop a Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e. multidimensional tensor) structure. As a motivating example we consider molecular data from multiple ‘omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficiency (ID) in a rhesus monkey model. We use a linear model with a low-rank structure on the coefficients to capture multi-way dependence and model the variance of the coefficients separately across each source to infer their relative contributions. Conjugate priors facilitate an efficient Gibbs sampling algorithm for posterior inference, assuming a continuous outcome with normal errors or a binary outcome with a probit link. Simulations demonstrate that our model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients, with large gains in performance by incorporating multiway structure and modest gains when accounting for differing signal sizes across the different sources. Moreover, it provides robust classification of ID monkeys for our motivating application.

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

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