Bayesian matrix completion for chemical activity using ToxCast data
High-throughput screening (HTS) is a technology that rapidly and efficiently screens thousands of chemicals for potential activity across different types of biological endpoints. Our goal is to derive posterior probabilities of activity for each chemical by assay endpoint pair, addressing the sparsity of HTS data. We propose a Bayesian hierarchical framework, which borrows information across different chemicals and assay endpoints and facilitates out-of-sample prediction of bioactivity potential for new chemicals. Furthermore, we make a novel attempt in toxicology to simultaneously model heteroscedastic errors as well as a nonparametric mean function, leading to a broader definition of activity.
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