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

TL;DR

This paper presents a Bayesian regression tree model that effectively links nanoparticles' physical and chemical properties to their toxicity profiles, handling small sample sizes and complex interactions.

Contribution

It introduces a novel Bayesian multiple regression tree approach combining threshold effects, interactions, and smoothing techniques for nanotoxicology data analysis.

Findings

Identified key physico-chemical properties influencing toxicity.

Demonstrated the model's ability to handle multiple doses and times.

Provided insights into nanoparticle toxicity mechanisms.

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.