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

@article{LowKam2015ABR, title={A Bayesian regression tree approach to identify the effect of nanoparticles’ properties on toxicity profiles}, author={C{\'e}cile Low-Kam and Donatello Telesca and Zhaoxia Ji and Haiyuan Zhang and Tian Xia and Jeffrey I. Zink and Andre E. Nel}, journal={The Annals of Applied Statistics}, year={2015}, volume={9}, pages={383-401} }

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…

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## References

SHOWING 1-10 OF 38 REFERENCES

### Relating Nanoparticle Properties to Biological Outcomes in Exposure Escalation Experiments.

- Environmental Science
- 2014

A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The ﬁrst line of screening for potentially…

### TOXICITY PROFILING OF ENGINEERED NANOMATERIALS VIA MULTIVARIATE DOSE-RESPONSE SURFACE MODELING.

- BiologyThe annals of applied statistics
- 2012

A hierarchical structure is used to account for the multivariate nature of the data by modeling dependence between outcomes and thereby combining information across cytotoxicity pathways, and a flexible surface-response model is provided that provides inference and generalizations of various classical risk assessment parameters.

### Relating nano‐particle properties to biological outcomes in exposure escalation experiments

- BiologyEnvironmetrics
- 2014

This work discusses a modeling strategy that relates the outcome of an exposure escalation experiment to nano‐particle properties, and makes use of a hierarchical decision process to identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physicochemical descriptors.

### Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles.

- ChemistrySmall
- 2011

It is important to recognize that a significantly larger data set would be needed in order to expand the applicability domain and increase the confidence and reliability of data-driven nanoSARs.

### Bayesian Treed Multivariate Gaussian Process With Adaptive Design: Application to a Carbon Capture Unit

- Computer ScienceTechnometrics
- 2014

A Bayesian treed multivariate Gaussian process (BTMGP) is developed as an extension of the Bayesian Treed Gaussian Process (BTGP) to model the cross-covariance function and the nonstationarity of the multivariate output and is compared with alternative approaches.

### Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models

- Computer Science
- 2010

The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis for inputs/covariates; sequential optimization of black-box functions; and a new Monte Carlo method for inference in multi-modal posterior distributions that combines simulated tempering and importance sampling.

### BART: Bayesian Additive Regression Trees

- Computer Science
- 2010

We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian…

### A Partially Linear Tree‐based Regression Model for Multivariate Outcomes

- BiologyBiometrics
- 2010

A novel tree‐based model provides a formal statistical testing framework for the evaluation of the association between a multivariate outcome and a set of candidate predictors, such as markers within a gene or pathway, while accommodating adjustment for other covariates.

### MULTIVARIATE REGRESSION TREES: A NEW TECHNIQUE FOR MODELING SPECIES–ENVIRONMENT RELATIONSHIPS

- Environmental Science
- 2002

Multivariate regression trees (MRT) are a new statistical technique that can be used to explore, describe, and predict relationships between multispecies data and environmental characteristics. MRT…

### Bayesian CART: Prior Specification and Posterior Simulation

- Computer Science
- 2007

The core computational innovations involve a novel Metropolis–Hastings method that can dramatically improve the convergence and mixing properties of MCMC methods of Bayesian CART analysis.