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Variable selection for BART: An application to gene regulation
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.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS755 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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