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10.1038/s41598-017-10491-y http://scihub22266oqcxt.onion/10.1038/s41598-017-10491-y C5583175!5583175 !28871116     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28871116 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28871116 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2017 ; 7 (1 ): 10327 Nephropedia Template TP {{tp|p=28871116 |t=2017. Controlling Directed Protein Interaction Networks in Cancer |pdf=|usr=}}{{28871116 }} gab.com Text Controlling Directed Protein Interaction Networks in Cancer JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28871116 #FOAvip Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine. Twit Text FOAVip Controlling Directed Protein Interaction Networks in Cancer ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28871116 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28871116 #FOAvip English Wikipedia <ref>{{Cite pmid|28871116 }}</ref> Controlling Directed Protein Interaction Networks in Cancer #MMPMID28871116 Kanhaiya K ; Czeizler E ; Gratie C ; Petre I Sci Rep 2017[Sep]; 7 (1 ): 10327 PMID28871116 show ga Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in each case a protein-protein interaction network and focus on the survivability-essential proteins specific to each cancer type. We show that these essential proteins are efficiently controllable from a relatively small computable set of driver nodes. Moreover, we adjust the method to find the driver nodes among FDA-approved drug-target nodes. We find that, while many of the drugs acting on the driver nodes are part of known cancer therapies, some of them are not used for the cancer types analyzed here; some drug-target driver nodes identified by our algorithms are not known to be used in any cancer therapy. Overall we show that a better understanding of the control dynamics of cancer through computational modelling can pave the way for new efficient therapeutic approaches and personalized medicine. |*Protein Interaction Mapping/methods [MESH] |*Protein Interaction Maps [MESH] |*Proteomics/methods [MESH] |Algorithms [MESH] |Databases, Genetic [MESH] |Humans [MESH] |Models, Biological [MESH] |Neoplasms/drug therapy/genetics/*metabolism [MESH] |Reproducibility of Results [MESH]Controlling Directed Protein Interaction Networks in Cancer (epmc).pdf DeepDyve Pubget Overpricing