{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T14:30:19Z","timestamp":1695306619565},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"23","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Set-based network similarity metrics are increasingly used to productively analyze genome-wide data. Conventional approaches, such as mean shortest path and clique-based metrics, have been useful but are not well suited to all applications. Computational scientists in other disciplines have developed communicability as a complementary metric. Network communicability considers all paths of all lengths between two network members. Given the success of previous network analyses of protein\u2013protein interactions, we applied the concepts of network communicability to this problem. Here we show that our communicability implementation has advantages over traditional approaches. Overall, analyses suggest network communicability has considerable utility in analysis of large-scale biological networks.<\/jats:p>\n               <jats:p>Availability and implementation: We provide our method as an R package for use in both human protein\u2013protein interaction network analyses and analyses of arbitrary networks along with a tutorial at http:\/\/www.shawlab.org\/NetComm\/.<\/jats:p>\n               <jats:p>Contact: \u00a0cashaw@bcm.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu536","type":"journal-article","created":{"date-parts":[[2014,8,15]],"date-time":"2014-08-15T05:12:51Z","timestamp":1408079571000},"page":"3387-3389","source":"Crossref","is-referenced-by-count":2,"title":["NetComm: a network analysis tool based on communicability"],"prefix":"10.1093","volume":"30","author":[{"given":"Ian M.","family":"Campbell","sequence":"first","affiliation":[{"name":"Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77054, USA"}]},{"given":"Regis A.","family":"James","sequence":"additional","affiliation":[{"name":"Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77054, USA"}]},{"given":"Edward S.","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77054, USA"}]},{"given":"Chad A.","family":"Shaw","sequence":"additional","affiliation":[{"name":"Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77054, USA"}]}],"member":"286","published-online":{"date-parts":[[2014,8,13]]},"reference":[{"key":"2023012712023217600_btu536-B1","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1038\/nprot.2008.100","article-title":"Network analysis tools: from biological networks to clusters and pathways","volume":"3","author":"Broh\u00e9e","year":"2008","journal-title":"Nat. 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