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Activity Number: 88
Type: Contributed
Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #310049
Title: A Bayesian Regression Tree Approach to Identify the Effect of Nanoparticles Properties on Toxicity Profiles
Author(s): Cecile Low-Kam*+ and Donatello Telesca and Zhaoxia Ji and Haiyuan Zhang and Tian Xia and Jeffrey I. Zink and Andre E. Nel
Companies: University of California, Los Angeles and University of California at Los Angeles and University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles
Keywords: Regression trees ; Bayesian CART ; P-splines ; Nanotoxicology

In nanotoxicology, exposure escalation assays describe how an organism is affected by a chemical compound, over multiple concentrations and times of exposure. We introduce a Bayesian multiple regression tree model to characterize relationships between physiochemical properties of nanoparticles and their toxicity, measured by these assays. 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 an 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 surfaces smoothing. The resulting posterior distribution is sampled via a Markov Chain Monte Carlo algorithm. This method allows for inference on a number of quantities of potential interest to substantive nanotoxicology, such as the importance of physiochemical 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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