  Bayesian Additive Regression Trees (BART) is a statistical sum of trees
model. It can be considered a Bayesian version of machine learning tree
ensemble methods where the individual trees are the base learners. However for
data sets where the number of variables $p$ is large (e.g. $p>5,000$) the
algorithm can become prohibitively expensive, computationally.
  Another method which is popular for high dimensional data is random forests,
a machine learning algorithm which grows trees using a greedy search for the
best split points. However, as it is not a statistical model, it cannot produce
probabilistic estimates or predictions.
  We propose an alternative algorithm for BART called BART-BMA, which uses
Bayesian Model Averaging and a greedy search algorithm to produce a model which
is much more efficient than BART for datasets with large $p$. BART-BMA
incorporates elements of both BART and random forests to offer a model-based
algorithm which can deal with high-dimensional data.
  We have found that BART-BMA can be run in a reasonable time on a standard
laptop for the "small $n$ large $p$" scenario which is common in many areas of
bioinformatics. We showcase this method using simulated data and data from two
real proteomic experiments; one to distinguish between patients with
cardiovascular disease and controls and another to classify agressive from
non-agressive prostate cancer. We compare our results to their main
competitors.
  Open source code written in R and Rcpp to run BART-BMA can be found at:
https://github.com/BelindaHernandez/BART-BMA.git
