The Annals of Applied Statistics

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, and Andre E. Nel

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Appl. Stat., Volume 9, Number 1 (2015), 383-401.

Dates
First available in Project Euclid: 28 April 2015

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1430226097

Digital Object Identifier
doi:10.1214/14-AOAS797

Mathematical Reviews number (MathSciNet)
MR3341120

Zentralblatt MATH identifier
06446573

Keywords
Bayesian CART nanotoxicology P-splines regression trees

Citation

Low-Kam, Cecile; Telesca, Donatello; Ji, Zhaoxia; Zhang, Haiyuan; Xia, Tian; Zink, Jeffrey I.; Nel, Andre E. A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles. Ann. Appl. Stat. 9 (2015), no. 1, 383--401. doi:10.1214/14-AOAS797. https://projecteuclid.org/euclid.aoas/1430226097


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