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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 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.

SUBMITTER: Li S 

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

REPOSITORIES: biostudies-literature

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

Li Shuang S   Hsu Li L   Peng Jie J   Wang Pei P  

The annals of applied statistics 20130301 1


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  ...[more]

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