We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis testing. Furthermore, this paper makes a novel attempt in toxicology to simultaneously model heteroscedastic errors and a nonparametric mean function, leading to a broader definition of activity whose need has been suggested by toxicologists. Real application identifies chemicals most likely active for neurodevelopmental disorders and obesity.
@article{arxiv.2009.08405,
title = {Bayesian Matrix Completion for Hypothesis Testing},
author = {Bora Jin and David B. Dunson and Julia E. Rager and David Reif and Stephanie M. Engel and Amy H. Herring},
journal= {arXiv preprint arXiv:2009.08405},
year = {2022}
}