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10.5483/BMBRep.2017.50.1.135 http://scihub22266oqcxt.onion/10.5483/BMBRep.2017.50.1.135 C5319659!5319659!27502015     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMB+Rep 2017 ; 50 (1): 12-9 Nephropedia Template TP {{tp|p=27502015|t=2017. Databases and tools for constructing signal transduction networks in cancer |pdf=|usr=}}{{27502015}} gab.com Text Databases and tools for constructing signal transduction networks in cancer JO: BMB Rep http://www.kidney.de/pget.php?&tw=1&pmid=27502015 #FOAvip Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., ?big data?), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called ?systems biology?. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets. Twit Text FOAVip Databases and tools for constructing signal transduction networks in cancer ????BMB Rep http://www.kidney.de/pget.php?&tw=1&pmid=27502015 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27502015 #FOAvip English Wikipedia <ref>{{Cite pmid|27502015}}</ref> Databases and tools for constructing signal transduction networks in cancer #MMPMID27502015 Nam S BMB Rep 2017[]; 50 (1): 12-9 PMID27502015show ga Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., ?big data?), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called ?systems biology?. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets. äDatabases and tools for constructing signal transduction networks in c(epmc).pdf DeepDyve Pubget Overpricing