2023 journal article

Bayesian matrix completion for hypothesis testing

Jin, B., Dunson, D. B., Rager, J. E., Reif, D. M., Engel, S. M., & Herring, A. H. (2023, March 15). JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, Vol. 3.

By: B. Jin*, D. Dunson*, J. Rager *, D. Reif n , S. Engel * & A. Herring *

topics (OpenAlex): Computational Drug Discovery Methods; Spectroscopy and Chemometric Analyses; Metabolomics and Mass Spectrometry Studies
TL;DR: 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. (via Semantic Scholar)
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Source: ORCID
Added: January 4, 2023

Abstract 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.