{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T12:31:49Z","timestamp":1723465909845},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: In a tri-partite biological network of transcription factors, their putative target genes, and the tissues in which the target genes are differentially expressed, a tightly inter-connected (dense) subgraph may reveal knowledge about tissue specific transcription regulation mediated by a specific set of transcription factors\u2014a tissue-specific transcriptional module. This is just one context in which an efficient computation of dense subgraphs in a multi-partite graph is needed.<\/jats:p>\n               <jats:p>Result: Here we report a generic stochastic search based method to compute dense subgraphs in a graph with an arbitrary number of partitions and an arbitrary connectivity among the partitions. We then use the tool to explore tissue-specific transcriptional regulation in the human genome. We validate our findings in Skeletal muscle based on literature. We could accurately deduce biological processes for transcription factors via the tri-partite clusters of transcription factors, genes, and the functional annotation of genes. Additionally, we propose a few previously unknown TF-pathway associations and tissue-specific roles for certain pathways. Finally, our combined analysis of Cardiac, Skeletal, and Smooth muscle data recapitulates the evolutionary relationship among the three tissues.<\/jats:p>\n               <jats:p>Contact: \u00a0sridharh@pcbi.upenn.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl260","type":"journal-article","created":{"date-parts":[[2006,7,27]],"date-time":"2006-07-27T15:38:03Z","timestamp":1154014683000},"page":"e117-e123","source":"Crossref","is-referenced-by-count":16,"title":["Dense subgraph computation via stochastic search: application to detect transcriptional modules"],"prefix":"10.1093","volume":"22","author":[{"given":"Logan","family":"Everett","sequence":"first","affiliation":[{"name":"Penn Center for Bioinformatics, University of Pennsylvania 1 \u00a0 1 \u00a0 \u00a0 Philadelphia, PA, USA 19104"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-San","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biology, University of Pennsylvania 2 \u00a0 2 \u00a0 \u00a0 Philadelphia, PA, USA 19104"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sridhar","family":"Hannenhalli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2006,7,15]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/14\/e117\/48841063\/bioinformatics_22_14_e117.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/22\/14\/e117\/48841063\/bioinformatics_22_14_e117.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T09:05:59Z","timestamp":1674551159000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/22\/14\/e117\/228638"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,7,15]]},"references-count":0,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2006,7,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btl260","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2006,7,15]]},"published":{"date-parts":[[2006,7,15]]}}}