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 ass(m)mes the overall sparsity of the partial correlation matrix and employs sparse regression techniques for model fitting.We illust rate the performance of "space" by cxtensive 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 "spacc" to a microarray breast cancer data set and identify a set of "hnb 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.