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10.1016/j.ygeno.2016.01.004 http://scihub22266oqcxt.onion/10.1016/j.ygeno.2016.01.004 C4761317!4761317!26773458     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 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genomics 2016 ; 107 (ä): 51-8 Nephropedia Template TP {{tp|p=26773458|t=2016. COPD subtypes identified by network-based clustering of blood gene expression |pdf=|usr=}}{{26773458}} gab.com Text COPD subtypes identified by network-based clustering of blood gene expression JO: Genomics http://www.kidney.de/pget.php?&tw=1&pmid=26773458 #FOAvip One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naïve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. Twit Text FOAVip COPD subtypes identified by network-based clustering of blood gene expression ????Genomics http://www.kidney.de/pget.php?&tw=1&pmid=26773458 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26773458 #FOAvip English Wikipedia <ref>{{Cite pmid|26773458}}</ref> COPD subtypes identified by network-based clustering of blood gene expression #MMPMID26773458 Chang Y; Glass K; Liu YY; Silverman EK; Crapo JD; Tal-Singer R; Bowler R; Dy J; Cho M; Castaldi P Genomics 2016[Mar]; 107 (ä): 51-8 PMID26773458show ga One of the most common smoking-related diseases, chronic obstructive pulmonary disease (COPD), results from a dysregulated, multi-tissue inflammatory response to cigarette smoke. We hypothesized that systemic inflammatory signals in genome-wide blood gene expression can identify clinically important COPD-related disease subtypes, and we leveraged pre-existing gene interaction networks to guide unsupervised clustering of blood microarray expression data. Using network-informed non-negative matrix factorization, we analyzed genome-wide blood gene expression from 229 former smokers in the ECLIPSE Study, and we identified novel, clinically relevant molecular subtypes of COPD. These network-informed clusters were more stable and more strongly associated with measures of lung structure and function than clusters derived from a network-naïve approach, and they were associated with subtype-specific enrichment for inflammatory and protein catabolic pathways. These clusters were successfully reproduced in an independent sample of 135 smokers from the COPDGene Study. äCOPD subtypes identified by network-based clustering of blood gene exp(epmc).pdf DeepDyve Pubget Overpricing