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http://scihub22266oqcxt.onion/ C6054478!6054478!30034908     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 282.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Quant+Biol 2017 ; 5 (4): 291-301 Nephropedia Template TP {{tp|p=30034908|t=2017. The Ontology of Biological and Clinical Statistics (OBCS)-based statistical method standardization and meta-analysis of host responses to yellow fever vaccines |pdf=|usr=}}{{30034908}} gab.com Text The Ontology of Biological and Clinical Statistics (OBCS)-based statistical method standardization and meta-analysis of host responses to yellow fever vaccines JO: Quant Biol http://www.kidney.de/pget.php?&tw=1&pmid=30034908 #FOAvip Background: The community-based Ontology of Biological and Clinical Statistics (OBCS) represents and standardizes biological and clinical data and statistical methods. Methods: Both OBCS and the Vaccine Ontology (VO) were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF-17D. A literature meta-analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. Results: A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables (e.g., microarray type and human variations). Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. Conclusions: The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology-based meta-analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods. Twit Text FOAVip The Ontology of Biological and Clinical Statistics (OBCS)-based statistical method standardization and meta-analysis of host responses to yellow fever vaccines ????Quant Biol http://www.kidney.de/pget.php?&tw=1&pmid=30034908 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30034908 #FOAvip English Wikipedia <ref>{{Cite pmid|30034908}}</ref> The Ontology of Biological and Clinical Statistics (OBCS)-based statistical method standardization and meta-analysis of host responses to yellow fever vaccines #MMPMID30034908 Zheng J; Li H; Liu Q; He Y Quant Biol 2017[Dec]; 5 (4): 291-301 PMID30034908show ga Background: The community-based Ontology of Biological and Clinical Statistics (OBCS) represents and standardizes biological and clinical data and statistical methods. Methods: Both OBCS and the Vaccine Ontology (VO) were used to ontologically model various components and relations in a typical host response to vaccination study. Such a model was then applied to represent and compare three microarray studies of host responses to the yellow fever vaccine YF-17D. A literature meta-analysis was then conducted to survey yellow fever vaccine response papers and summarize statistical methods, using OBCS. Results: A general ontological model was developed to identify major components in a typical host response to vaccination. Our ontology modeling of three similar studies identified common and different components which may contribute to varying conclusions. Although these three studies all used the same vaccine, human blood samples, similar sample collection time post vaccination, and microarray assays, statistically differentially expressed genes and associated gene functions differed, likely due to the differences in specific variables (e.g., microarray type and human variations). Our manual annotation of 95 papers in human responses to yellow fever vaccines identified 38 data analysis methods. These statistical methods were consistently represented and classified with OBCS. Eight statistical methods not available in existing ontologies were added to OBCS. Conclusions: The study represents the first single use case of applying OBCS ontology to standardize, integrate, and use biomedical data and statistical methods. Our ontology-based meta-analysis showed that different experimental results might be due to different experimental assays and conditions, sample variations, and data analysis methods. äThe Ontology of Biological and Clinical Statistics (OBCS)-based statis(epmc).pdf DeepDyve Pubget Overpricing