{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T10:52:41Z","timestamp":1773399161413,"version":"3.50.1"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1752692"],"award-info":[{"award-number":["DMS-1752692"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB026936"],"award-info":[{"award-number":["R01EB026936"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM135928"],"award-info":[{"award-number":["R01GM135928"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Biclustering is a generalization of clustering used to identify simultaneous grouping patterns in observations (rows) and features (columns) of a data matrix. Recently, the biclustering task has been formulated as a convex optimization problem. While this convex recasting of the problem has attractive properties, existing algorithms do not scale well. To address this problem and make convex biclustering a practical tool for analyzing larger data, we propose an implementation of fast convex biclustering called COBRAC to reduce the computing time by iteratively compressing problem size along with the solution path. We apply COBRAC to several gene expression datasets to demonstrate its effectiveness and efficiency. Besides the standalone version for COBRAC, we also developed a related online web server for online calculation and visualization of the downloadable interactive results.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and test data are available at https:\/\/github.com\/haidyi\/cvxbiclustr or https:\/\/zenodo.org\/record\/4620218. The web server is available at https:\/\/cvxbiclustr.ericchi.com.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab248","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T22:04:36Z","timestamp":1619129076000},"page":"3667-3669","source":"Crossref","is-referenced-by-count":7,"title":["COBRAC: a fast implementation of convex biclustering with compression"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2591-1922","authenticated-orcid":false,"given":"Haidong","family":"Yi","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599, USA"}]},{"given":"Le","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Genetics, Curriculum in Bioinformatics & Computational Biology, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599, USA"}]},{"given":"Gal","family":"Mishne","sequence":"additional","affiliation":[{"name":"Hal\u0131c\u0131o\u011flu\u00a0Data Science Institute, University of California , San Diego, La Jolla, CA 92093, USA"}]},{"given":"Eric C","family":"Chi","sequence":"additional","affiliation":[{"name":"Department of Statistics, North Carolina State University , Raleigh, NC 27607, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"2023051701221481800_btab248-B1","doi-asserted-by":"crossref","first-page":"e1004228","DOI":"10.1371\/journal.pcbi.1004228","article-title":"Convex clustering: an attractive alternative to hierarchical clustering","volume":"11","author":"Chen","year":"2015","journal-title":"PLoS Comput. 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