{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T13:26:55Z","timestamp":1724592415379},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2018,7,13]],"date-time":"2018-07-13T00:00:00Z","timestamp":1531440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Large-scale gene expression analysis is a valuable asset for data-driven hypothesis generation. However, the convoluted nature of large expression datasets often hinders extraction of meaningful biological information.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To this end, we developed GECO, a gene expression correlation analysis software that uses a genetic algorithm-driven approach to deconvolute complex expression datasets into two subpopulations that display positive and negative correlations between a pair of queried genes. GECO\u2019s mutational enrichment and pairwise drug sensitivity analyses functions that follow the deconvolution step may help to identify the mutational factors that drive the gene expression correlation in the generated subpopulations and their differential drug vulnerabilities. Finally, GECO\u2019s drug sensitivity screen function can be used to identify drugs that differentially affect the subpopulations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>http:\/\/www.proteinguru.com\/geco\/ and http:\/\/www.proteinguru.com\/geco\/codes\/<\/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\/bty623","type":"journal-article","created":{"date-parts":[[2018,7,13]],"date-time":"2018-07-13T11:28:47Z","timestamp":1531481327000},"page":"156-159","source":"Crossref","is-referenced-by-count":4,"title":["GECO: gene expression correlation analysis after genetic algorithm-driven deconvolution"],"prefix":"10.1093","volume":"35","author":[{"given":"Jamil","family":"Najafov","sequence":"first","affiliation":[{"name":"Department of Cell Biology, Harvard Medical School, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayaz","family":"Najafov","sequence":"additional","affiliation":[{"name":"Department of Cell Biology, Harvard Medical School, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,7,13]]},"reference":[{"key":"2023013107215924600_bty623-B1","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023013107215924600_bty623-B2","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1074\/jbc.M209677200","article-title":"BMS-345541 is a highly selective inhibitor of I kappa B kinase that binds at an allosteric site of the enzyme and blocks NF-kappa B-dependent transcription in mice","volume":"278","author":"Burke","year":"2003","journal-title":"J. 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