Estimation of directed subnetworks in ultra high dimensional data for gene network problems
- Authors
- Han, Sung Won; Kim, SungHwan; Seok, Junhee; Yoon, Jeewhan; Zhong, Hua
- Issue Date
- 2017
- Publisher
- INT PRESS BOSTON, INC
- Keywords
- Bayesian network; Directed acyclic graph; Penalized likelihood; High dimension; Subnetworks
- Citation
- STATISTICS AND ITS INTERFACE, v.10, no.4, pp.657 - 676
- Indexed
- SCIE
SCOPUS
- Journal Title
- STATISTICS AND ITS INTERFACE
- Volume
- 10
- Number
- 4
- Start Page
- 657
- End Page
- 676
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/86401
- DOI
- 10.4310/SII.2017.v10.n4.a10
- ISSN
- 1938-7989
- Abstract
- 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.
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- Appears in
Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
- College of Engineering > School of Electrical Engineering > 1. Journal Articles
- Graduate School > Graduate School of management of technology > 1. Journal Articles
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