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Exhaustive search is infeasible for transcriptomic datasets also; since for [Formula: see text] genes, there are [Formula: see text] possible gene combinations. Our method builds three filters to avoid computing the association for a large fraction of the gene combinations, which do not produce high correlation. Our experiments on a synthetic dataset and a prostate cancer dataset demonstrate that our method produces accurate results at the transcriptome level in practical time. Moreover, our method identifies biologically novel results which classical pairwise gene association studies are unlikely to discover. <\/jats:p>","DOI":"10.1142\/s021972001840019x","type":"journal-article","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T23:24:20Z","timestamp":1537399460000},"page":"1840019","source":"Crossref","is-referenced-by-count":1,"title":["Identification of jointly correlated gene sets"],"prefix":"10.1142","volume":"16","author":[{"given":"Yuanfang","family":"Ren","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA"}]},{"given":"Ahmet","family":"Ay","sequence":"additional","affiliation":[{"name":"Departments of Biology and Mathematics, Colgate University, Hamilton, NY 13346, USA"}]},{"given":"Travis A.","family":"Gerke","sequence":"additional","affiliation":[{"name":"Moffitt Cancer Center, Tampa, FL 33607, 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