Han, Sung Won; Kim, Sunghwan; Seok, Junhee; Yoon, Jeewhan; Zhong, Hua Estimation of directed subnetworks in ultra-high dimensional data for gene network problems. (English) Zbl 1387.92034 Stat. Interface 10, No. 4, 657-676 (2017). Summary: The next generation sequencing technology generates ultra-high dimensional data. However, it is computationally impractical to estimate an entire directed acyclic graph (DAG) under such high dimensionality. In this paper, we discuss two different types of problems to estimate subnetworks in ultra high dimensional data. The first problem is to estimate DAGs of a subnetwork adjacent to a target gene, and the second problem is to estimate DAGs of multiple subnetworks without information about a target gene. To address each problem, we propose efficient methods to estimate subnetworks by using layer-dependent weights with BIC criteria or by using community detection approaches to identify clusters as subnetworks. We apply such approaches to the gene expression data of breast cancer in TCGA as a practical example. MSC: 92C40 Biochemistry, molecular biology 62P10 Applications of statistics to biology and medical sciences; meta analysis Keywords:Bayesian network; directed acyclic graph; penalized likelihood; high dimension; subnetworks PDFBibTeX XMLCite \textit{S. W. Han} et al., Stat. Interface 10, No. 4, 657--676 (2017; Zbl 1387.92034) Full Text: DOI