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March 2015 A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles
Cecile Low-Kam, Donatello Telesca, Zhaoxia Ji, Haiyuan Zhang, Tian Xia, Jeffrey I. Zink, Andre E. Nel
Ann. Appl. Stat. 9(1): 383-401 (March 2015). DOI: 10.1214/14-AOAS797


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.


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Cecile Low-Kam. Donatello Telesca. Zhaoxia Ji. Haiyuan Zhang. Tian Xia. Jeffrey I. Zink. Andre E. Nel. "A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles." Ann. Appl. Stat. 9 (1) 383 - 401, March 2015.


Published: March 2015
First available in Project Euclid: 28 April 2015

zbMATH: 06446573
MathSciNet: MR3341120
Digital Object Identifier: 10.1214/14-AOAS797

Keywords: Bayesian CART , nanotoxicology , p-splines , regression trees

Rights: Copyright © 2015 Institute of Mathematical Statistics


Vol.9 • No. 1 • March 2015
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