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With gene expression, network analysis, which takes a system perspective and examines the interconnections among genes, has been established as highly important and meaningful. In the construction of gene expression networks, a commonly adopted technique is high\u2010dimensional regularized regression. Network construction can be unadjusted (which focuses on gene expressions only) and adjusted (which also incorporates regulators of gene expressions), and the two types of construction have different implications and can be equally important. In this article, we propose a variable selection hierarchy to connect the unadjusted regression\u2010based network construction with the adjusted construction that incorporates two or more types of regulators. This hierarchy is sensible and amounts to additional information for both constructions, thus having the potential of improving variable selection and estimation. An effective computational algorithm is developed, and extensive simulation demonstrates the superiority of the proposed construction over multiple closely relevant alternatives. The analysis of TCGA data further demonstrates the practical utility of the proposed approach.<\/jats:p>","DOI":"10.1002\/sam.11609","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T06:33:37Z","timestamp":1672900417000},"page":"272-294","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchy\u2010assisted gene expression regulatory network analysis"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8460-9504","authenticated-orcid":false,"given":"Han","family":"Yan","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences University of Chinese Academy of Sciences  Beijing China"},{"name":"Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences  Beijing China"},{"name":"Department of Biostatistics Yale School of Public Health  New Haven Connecticut USA"}]},{"given":"Sanguo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences University of Chinese Academy of Sciences  Beijing China"},{"name":"Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences  Beijing China"},{"name":"Pazhou Lab  Guangzhou China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9001-4999","authenticated-orcid":false,"given":"Shuangge","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Yale School of Public Health  New Haven Connecticut USA"}]}],"member":"311","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"J.Antonelli SSGL: Spike and Slab Group Lasso. 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