317 research outputs found
Discovering Graphical Granger Causality Using the Truncating Lasso Penalty
Components of biological systems interact with each other in order to carry
out vital cell functions. Such information can be used to improve estimation
and inference, and to obtain better insights into the underlying cellular
mechanisms. Discovering regulatory interactions among genes is therefore an
important problem in systems biology. Whole-genome expression data over time
provides an opportunity to determine how the expression levels of genes are
affected by changes in transcription levels of other genes, and can therefore
be used to discover regulatory interactions among genes.
In this paper, we propose a novel penalization method, called truncating
lasso, for estimation of causal relationships from time-course gene expression
data. The proposed penalty can correctly determine the order of the underlying
time series, and improves the performance of the lasso-type estimators.
Moreover, the resulting estimate provides information on the time lag between
activation of transcription factors and their effects on regulated genes. We
provide an efficient algorithm for estimation of model parameters, and show
that the proposed method can consistently discover causal relationships in the
large , small setting. The performance of the proposed model is
evaluated favorably in simulated, as well as real, data examples. The proposed
truncating lasso method is implemented in the R-package grangerTlasso and is
available at http://www.stat.lsa.umich.edu/~shojaie.Comment: 12 pages, 4 figures, 1 tabl
Optimal experiment design in a filtering context with application to sampled network data
We examine the problem of optimal design in the context of filtering multiple
random walks. Specifically, we define the steady state E-optimal design
criterion and show that the underlying optimization problem leads to a second
order cone program. The developed methodology is applied to tracking network
flow volumes using sampled data, where the design variable corresponds to
controlling the sampling rate. The optimal design is numerically compared to a
myopic and a naive strategy. Finally, we relate our work to the general problem
of steady state optimal design for state space models.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS283 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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