{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T06:58:10Z","timestamp":1790924290547,"version":"4.1.0"},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"15","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2004,10,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Several pattern discovery methods have been proposed to detect over-represented motifs in upstream sequences of co-regulated genes, and are for example used to predict cis-acting elements from clusters of co-expressed genes. The clusters to be analyzed are often noisy, containing a mixture of co-regulated and non-co-regulated genes. We propose a method to discriminate co-regulated from non-co-regulated genes on the basis of counts of pattern occurrences in their non-coding sequences.<\/jats:p>\n               <jats:p>Methods: String-based pattern discovery is combined with discriminant analysis to classify genes on the basis of putative regulatory motifs.<\/jats:p>\n               <jats:p>Results: The approach is evaluated by comparing the significance of patterns detected in annotated regulons (positive control), random gene selections (negative control) and high-throughput regulons (noisy data) from the yeast Saccharomyces cerevisiae. The classification is evaluated on the annotated regulons, and the robustness and rejection power is assessed with mixtures of co-regulated and random genes.<\/jats:p>\n               <jats:p>Supplementary information: \u00a0http:\/\/rsat.ulb.ac.be\/rsat\/<\/jats:p>","DOI":"10.1093\/bioinformatics\/bth252","type":"journal-article","created":{"date-parts":[[2004,4,13]],"date-time":"2004-04-13T00:34:51Z","timestamp":1081816491000},"page":"2370-2379","source":"Crossref","is-referenced-by-count":21,"title":["Combining pattern discovery and discriminant analysis to predict gene co-regulation"],"prefix":"10.1093","volume":"20","author":[{"given":"N.","family":"Simonis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. J.","family":"Wodak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G. N.","family":"Cohen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"van Helden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2004,4,8]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/15\/2370\/48906025\/bioinformatics_20_15_2370.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/20\/15\/2370\/48906025\/bioinformatics_20_15_2370.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T16:05:28Z","timestamp":1674662728000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/20\/15\/2370\/233071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,4,8]]},"references-count":0,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2004,10,12]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bth252","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2004,10,12]]},"published":{"date-parts":[[2004,4,8]]}}}