The development of high throughput screening (HTS) assays in the field of nanotoxicology provide new opportunities for the hazard assessment and ranking of engineered nanomaterials (ENMs). It is often necessary to rank lists of materials based on multiple risk assessment parameters, often aggregated across several measures of toxicity and possibly spanning an array of experimental platforms. Bayesian models coupled with the optimization of loss functions have been shown to provide an effective framework for conducting inference on ranks. In this article we present various loss-function-based ranking approaches for comparing ENM within experiments and toxicity parameters. Additionally, we propose a framework for the aggregation of ranks across different sources of evidence while allowing for differential weighting of this evidence based on its reliability and importance in risk ranking. We apply these methods to high throughput toxicity data on two human cell-lines, exposed to eight different nanomaterials, and measured in relation to four cytotoxicity outcomes. This article has supplementary material online.

中文翻译：

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分层等级聚合在纳米毒理学中的应用。

纳米毒理学领域高通量筛选 (HTS) 检测的发展为工程纳米材料 (ENM) 的危害评估和排序提供了新的机会。通常需要根据多个风险评估参数对材料列表进行排名，这些参数通常跨多个毒性度量汇总，并可能跨越一系列实验平台。贝叶斯模型与损失函数的优化相结合，已被证明为进行秩推理提供了有效的框架。在本文中，我们提出了各种基于损失函数的排序方法，用于在实验和毒性参数中比较 ENM。此外，我们提出了一个框架，用于汇总不同证据来源的排名，同时允许根据其在风险排名中的可靠性和重要性对该证据进行不同的加权。我们将这些方法应用于两种人类细胞系的高通量毒性数据，暴露于八种不同的纳米材料，并根据四种细胞毒性结果进行测量。这篇文章在网上有补充材料。