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10.1016/j.gde.2016.02.002 http://scihub22266oqcxt.onion/10.1016/j.gde.2016.02.002 C4943455!4943455!26950762     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Opin+Genet+Dev 2016 ; 37 (ä): 101-8 Nephropedia Template TP {{tp|p=26950762|t=2016. Understanding transcriptional regulatory networks using computational models |pdf=|usr=}}{{26950762}} gab.com Text Understanding transcriptional regulatory networks using computational models JO: Curr Opin Genet Dev http://www.kidney.de/pget.php?&tw=1&pmid=26950762 #FOAvip Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs. Here, we survey current computational methods for inferring TRN models using genome-scale data. We discuss their advantages and limitations. We summarize representative TRNs constructed using genome-scale data in both normal and disease development. We discuss lessons learned about the structure/function relationship of TRNs, based on examining various large-scale TRN models. Finally, we outline some open questions regarding TRNs, including how to improve model accuracy by integrating complementary data types, how to infer condition-specific TRNs, and how to compare TRNs across conditions and species in order to understand their structure/function relationship. Twit Text FOAVip Understanding transcriptional regulatory networks using computational models ????Curr Opin Genet Dev http://www.kidney.de/pget.php?&tw=1&pmid=26950762 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26950762 #FOAvip English Wikipedia <ref>{{Cite pmid|26950762}}</ref> Understanding transcriptional regulatory networks using computational models #MMPMID26950762 He B; Tan K Curr Opin Genet Dev 2016[Apr]; 37 (ä): 101-8 PMID26950762show ga Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs. Here, we survey current computational methods for inferring TRN models using genome-scale data. We discuss their advantages and limitations. We summarize representative TRNs constructed using genome-scale data in both normal and disease development. We discuss lessons learned about the structure/function relationship of TRNs, based on examining various large-scale TRN models. Finally, we outline some open questions regarding TRNs, including how to improve model accuracy by integrating complementary data types, how to infer condition-specific TRNs, and how to compare TRNs across conditions and species in order to understand their structure/function relationship. äUnderstanding transcriptional regulatory networks using computational (epmc).pdf DeepDyve Pubget Overpricing