BOOTSTRAP INFERENCE FOR NETWORK CONSTRUCTION WITH AN APPLICATION TO A BREAST CANCER MICROARRAY STUDY.

ABSTRACT: 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

SUBMITTER: Li S 

PROVIDER: S-EPMC3930359 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature