  In this paper, we propose a computationally efficient approach --
space(Sparse PArtial Correlation Estimation)-- for selecting non-zero partial
correlations under the high-dimension-low-sample-size setting. This method
assumes the overall sparsity of the partial correlation matrix and employs
sparse regression techniques for model fitting. We illustrate the performance
of space by extensive simulation studies. It is shown that space performs well
in both non-zero partial correlation selection and the identification of hub
variables, and also outperforms two existing methods. We then apply space to a
microarray breast cancer data set and identify a set of hub genes which may
provide important insights on genetic regulatory networks. Finally, we prove
that, under a set of suitable assumptions, the proposed procedure is
asymptotically consistent in terms of model selection and parameter estimation.
