A key aim in system biology is to understand molecules’ structural and functional processes in a living cell. With the development of high-throughput technologies, quantitative methods can be applied on large scale ‘omics’ datasets. Due to the nature of intricate relationships of all molecules in a cell, network-based methods have become a popular approach to reconstruct gene-gene, gene-protein, and protein-protein interactions. Among different network approaches, Gaussian Graphical Model shows advantages in reconstructing gene co-expression networks because it is able to capture the direct association between genes with partial correlations. However, estimating and inferring partial correlations under the high-dimensional setting are very challenging. A method utilizing penalized partial correlations called exact hypothesis testing for shrinkage based Gaussian graphical models (Shrunk MLE) is able to overcome the high-dimension problem. However, the statistical inference of such penalized partial correlations is not satisfying. In this project, a novel network inference method, named c-level Partial Correlation Graph (c-level PCG), is applied to the gene expression dataset to model gene-gene direct association. It overcomes the ill-condition of p greater than n and successfully infers estimated partial correlation with false discovery rate controlled. Compared to Shrunk MLE, c-level PCG is able to achieve much higher statistical power and control the false discovery rate at the same time, according to our simulation studies.