Open Access
September 2014 Variable selection for BART: An application to gene regulation
Justin Bleich, Adam Kapelner, Edward I. George, Shane T. Jensen
Ann. Appl. Stat. 8(3): 1750-1781 (September 2014). DOI: 10.1214/14-AOAS755


We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat. 4 (2010) 266–298] provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (Saccharomyces cerevisiae). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods. The methods developed in this work are readily available in the R package bartMachine.


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Justin Bleich. Adam Kapelner. Edward I. George. Shane T. Jensen. "Variable selection for BART: An application to gene regulation." Ann. Appl. Stat. 8 (3) 1750 - 1781, September 2014.


Published: September 2014
First available in Project Euclid: 23 October 2014

zbMATH: 1304.62132
MathSciNet: MR3271352
Digital Object Identifier: 10.1214/14-AOAS755

Keywords: Bayesian learning , decision trees , gene regulatory network , machine learning , Nonparametric regression , permutation testing , Variable selection

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 3 • September 2014
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