{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T20:10:20Z","timestamp":1772568620702,"version":"3.50.1"},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["22A0101"],"award-info":[{"award-number":["22A0101"]}]},{"name":"Scientific Research Fund of Hunan Provincial Education Department","award":["22A0350"],"award-info":[{"award-number":["22A0350"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology\u2019s optimisation and the global topology\u2019s maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.<\/jats:p>","DOI":"10.1093\/bib\/bbad129","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T17:03:27Z","timestamp":1682010207000},"source":"Crossref","is-referenced-by-count":28,"title":["NSRGRN: a network structure refinement method for gene regulatory network inference"],"prefix":"10.1093","volume":"24","author":[{"given":"Wei","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University , Xiangtan 411105 , China"},{"name":"School of Computer Science, Xiangtan University , Xiangtan 411105 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University , Xiangtan 411105 , China"},{"name":"School of Computer Science, Xiangtan University , Xiangtan 411105 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University , Guangzhou 510665 , China"},{"name":"Guangdong Provincial Key Laboratory of Intellectual Property Big Data , Guangzhou 510665 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangzheng","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha 410000 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiqing","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University , Xiangtan 411105 , China"},{"name":"School of Computer Science, Xiangtan University , Xiangtan 411105 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University , Xiangtan 411105 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology , Xiangtan 411201 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"issue":"51","key":"2023052022124680300_ref1","doi-asserted-by":"crossref","first-page":"20063","DOI":"10.1073\/pnas.0806007105","article-title":"Properties of developmental gene regulatory 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