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10.1038/srep11432 http://scihub22266oqcxt.onion/10.1038/srep11432 C4464350!4464350!26066708     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2015 ; 5 (ä): ä Nephropedia Template TP {{tp|p=26066708|t=2015. TRRUST: a reference database of human transcriptional regulatory interactions |pdf=|usr=}}{{26066708}} gab.com Text TRRUST: a reference database of human transcriptional regulatory interactions JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26066708 #FOAvip The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs. Twit Text FOAVip TRRUST: a reference database of human transcriptional regulatory interactions ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=26066708 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26066708 #FOAvip English Wikipedia <ref>{{Cite pmid|26066708}}</ref> TRRUST: a reference database of human transcriptional regulatory interactions #MMPMID26066708 Han H; Shim H; Shin D; Shim JE; Ko Y; Shin J; Kim H; Cho A; Kim E; Lee T; Kim H; Kim K; Yang S; Bae D; Yun A; Kim S; Kim CY; Cho HJ; Kang B; Shin S; Lee I Sci Rep 2015[]; 5 (ä): ä PMID26066708show ga The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs. äTRRUST: a reference database of human transcriptional regulatory inter(epmc).pdf DeepDyve Pubget Overpricing