9,782 research outputs found
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
Is There Enhanced Depletion of Gas-Phase Nitrogen in Moderately Reddened Lines of Sight?
We report on the abundance of interstellar neutral nitrogen (NI) for 30
sightlines, using data from the Far Ultraviolet Spectroscopic Explorer (FUSE)
and the Hubble Space Telescope (HST). NI column densities are derived by
measuring the equivalent widths of several ultraviolet absorption lines and
subsequently fitting those to a curve of growth. We find a mean interstellar
N/H of 51+/-4 ppm. This is below the mean found by Meyer et al. of 62(+4,-3)
ppm (adjusted for a difference in f-values). Our mean N/H is similar, however,
to the (f-value adjusted) mean of 51+/-3 ppm found by Knauth et al. for a
larger sample of sightlines with larger hydrogen column densities comparable to
those in this study. We discuss the question of whether or not nitrogen shows
increased gas-phase depletion in lines of sight with column densities
log(H_tot) >~ 21, as claimed by Knauth et al. The nitrogen abundance in the
line of sight toward HD 152236 is particularly interesting. We derive very
small N/H and N/O ratios for this line of sight that may support a previous
suggestion that members of the Sco OB1 association formed from an N-deficient
region.Comment: Accepted in The Astrophysical Journal, 9/2006 (expected pub. date:
1/2007) 38 pages, 5 figures (4 color
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