标题： 一种贝叶斯回归树方法用于识别纳米颗粒属性对毒性轮廓的影响 显示英文标题

标题： A Bayesian regression tree approach to identify the effect of nanoparticles' properties on toxicity profiles

摘要： 我们引入了一种贝叶斯多回归树模型，用于描述纳米颗粒的理化性质与其体外毒性在多个剂量和暴露时间下的关系。与依赖数据汇总的传统模型不同，我们的模型通过结合来自一般暴露实验中所有剂量、暴露时间和重复测量的数据，解决了样本量小的问题，并避免了信息的任意丢失。该技术结合了贝叶斯树来建模阈值效应和交互作用，并使用惩罚性B样条平滑剂量-时间反应表面。后验分布通过马尔可夫链蒙特卡罗方法进行采样。此方法允许对实质性纳米毒理学中许多潜在感兴趣的数量进行推断，例如理化性质的重要性及其对毒性的边际影响。我们以24种纳米金属氧化物的文库分析为例展示了该方法的应用。 显示英文摘要

摘要： We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose- and time-response surface smoothing. The resulting posterior distribution is sampled by Markov Chain Monte Carlo. This method allows for inference on a number of quantities of potential interest to substantive nanotoxicology, such as the importance of physico-chemical properties and their marginal effect on toxicity. We illustrate the application of our method to the analysis of a library of 24 nano metal oxides.