{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T22:24:09Z","timestamp":1758925449389},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: R\/EBcoexpress implements the approach of Dawson and Kendziorski using R, a freely available, open source statistical programming language. The approach identifies differential co-expression (DC) by examining the correlations among gene pairs using an empirical Bayesian approach, producing a false discovery rate controlled list of DC pairs. This interrogation of DC gene pairs complements but is distinct from differential expression analyses, under the general goal of understanding differential regulation across biological conditions.<\/jats:p>\n               <jats:p>Availability and implementation: R\/EBcoexpress is freely available and hosted on Bioconductor; a source file and vignette may be found at http:\/\/www.bioconductor.org\/packages\/release\/bioc\/html\/EBcoexpress.html<\/jats:p>\n               <jats:p>Contact: \u00a0DrJADawson@hotmail.com or kendzior@wisc.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts268","type":"journal-article","created":{"date-parts":[[2012,5,18]],"date-time":"2012-05-18T01:07:57Z","timestamp":1337303277000},"page":"1939-1940","source":"Crossref","is-referenced-by-count":36,"title":["R\/EBcoexpress: an empirical Bayesian framework for discovering differential co-expression"],"prefix":"10.1093","volume":"28","author":[{"given":"John A.","family":"Dawson","sequence":"first","affiliation":[{"name":"1 Statistics and 2Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyun","family":"Ye","sequence":"additional","affiliation":[{"name":"1 Statistics and 2Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christina","family":"Kendziorski","sequence":"additional","affiliation":[{"name":"1 Statistics and 2Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2012,5,16]]},"reference":[{"key":"2023012512432459600_B1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on bias and variance","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2023012512432459600_B2","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1093\/bioinformatics\/bti722","article-title":"Differential coexpression analysis using microarray data and its application to human cancer","volume":"21","author":"Choi","year":"2005","journal-title":"Bioinformatics"},{"key":"2023012512432459600_B3","article-title":"An empirical Bayesian approach for identifying differential co-expression in high-throughput experiments","author":"Dawson","year":"2011","journal-title":"Biometrics."},{"key":"2023012512432459600_B4","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.tig.2010.05.001","article-title":"From \u2018differential expression\u2019 to \u2018differential networking\u2019 \u2013 identification of dysfunctional regulatory networks in diseases","volume":"26","author":"de la","year":"2010","journal-title":"Trends Genet."},{"key":"2023012512432459600_B5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1371\/journal.pcbi.1000382","article-title":"A differential wiring analysis of expression data correctly identifies the causal mutation","volume":"5","author":"Hudson","year":"2009","journal-title":"PLoS Comp. 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