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10.1038/s41598-017-00996-x http://scihub22266oqcxt.onion/10.1038/s41598-017-00996-x C5430701!5430701!28432361     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2017 ; 7 (ä): ä Nephropedia Template TP {{tp|p=28432361|t=2017. Disease biomarker identification from gene network modules for metastasized breast cancer |pdf=|usr=}}{{28432361}} gab.com Text Disease biomarker identification from gene network modules for metastasized breast cancer JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28432361 #FOAvip Advancement in science has tended to improve treatment of fatal diseases such as cancer. A major concern in the area is the spread of cancerous cells, technically refered to as metastasis into other organs beyond the primary organ. Treatment in such a stage of cancer is extremely difficult and usually palliative only. In this study, we focus on finding gene-gene network modules which are functionally similar in nature in the case of breast cancer. These modules extracted during the disease progression stages are analyzed using p-value and their associated pathways. We also explore interesting patterns associated with the causal genes, viz., SCGB1D2, MET, CYP1B1 and MMP9 in terms of expression similarity and pathway contexts. We analyze the genes involved in both the stages? non metastasis and metastatsis and change in their expression values, their associated pathways and roles as the disease progresses from one stage to another. We discover three additional pathways viz., Glycerophospholipid metablism, h-Efp pathway and CARM1 and Regulation of Estrogen Receptor, which can be related to the metastasis phase of breast cancer. These new pathways can be further explored to identify their relevance during the progression of the disease. Twit Text FOAVip Disease biomarker identification from gene network modules for metastasized breast cancer ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28432361 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28432361 #FOAvip English Wikipedia <ref>{{Cite pmid|28432361}}</ref> Disease biomarker identification from gene network modules for metastasized breast cancer #MMPMID28432361 Sharma P; Bhattacharyya DK; Kalita J Sci Rep 2017[]; 7 (ä): ä PMID28432361show ga Advancement in science has tended to improve treatment of fatal diseases such as cancer. A major concern in the area is the spread of cancerous cells, technically refered to as metastasis into other organs beyond the primary organ. Treatment in such a stage of cancer is extremely difficult and usually palliative only. In this study, we focus on finding gene-gene network modules which are functionally similar in nature in the case of breast cancer. These modules extracted during the disease progression stages are analyzed using p-value and their associated pathways. We also explore interesting patterns associated with the causal genes, viz., SCGB1D2, MET, CYP1B1 and MMP9 in terms of expression similarity and pathway contexts. We analyze the genes involved in both the stages? non metastasis and metastatsis and change in their expression values, their associated pathways and roles as the disease progresses from one stage to another. We discover three additional pathways viz., Glycerophospholipid metablism, h-Efp pathway and CARM1 and Regulation of Estrogen Receptor, which can be related to the metastasis phase of breast cancer. These new pathways can be further explored to identify their relevance during the progression of the disease. äDisease biomarker identification from gene network modules for metasta(epmc).pdf DeepDyve Pubget Overpricing