  Gaussian Graphical Models (GGMs) have been used to construct genetic
regulatory networks where regularization techniques are widely used since the
network inference usually falls into a high-dimension-low-sample-size scenario.
Yet, finding the right amount of regularization can be challenging, especially
in an unsupervised setting where traditional methods such as BIC or
cross-validation often do not work well. In this paper, we propose a new method
- Bootstrap Inference for Network COnstruction (BINCO) - to infer networks by
directly controlling the false discovery rates (FDRs) of the selected edges.
This method fits a mixture model for the distribution of edge selection
frequencies to estimate the FDRs, where the selection frequencies are
calculated via model aggregation. This method is applicable to a wide range of
applications beyond network construction. When we applied our proposed method
to building a gene regulatory network with microarray expression breast cancer
data, we were able to identify high-confidence edges and well-connected hub
genes that could potentially play important roles in understanding the
underlying biological processes of breast cancer.
