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.

中文翻译：

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用于假设检验的贝叶斯矩阵补全。

我们的目标是通过分析终点组合来推断每种化学物质的生物活性，解决毒理学数据的稀疏性问题。我们提出了一个贝叶斯层次框架，它借用不同化学品和测定端点的信息，促进对尚未测定的化学品的活动进行样本外预测，量化预测活动的不确定性，并调整假设检验的多重性。此外，本文在毒理学方面进行了一项新颖的尝试，即同时对异方差误差和非参数均值函数进行建模，从而对毒理学家提出的需要的活动进行了更广泛的定义。实际应用确定了最有可能对神经发育障碍和肥胖症有活性的化学物质。