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10.1186/s13040-017-0137-5 http://scihub22266oqcxt.onion/10.1186/s13040-017-0137-5 C5446754!5446754!28559929     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BioData+Min 2017 ; 10 (ä): ä Nephropedia Template TP {{tp|p=28559929|t=2017. Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype |pdf=|usr=}}{{28559929}} gab.com Text Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype JO: BioData Min http://www.kidney.de/pget.php?&tw=1&pmid=28559929 #FOAvip Background: Genetic studies for complex diseases have predominantly discovered main effects at individual loci, but have not focused on genomic and environmental contexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims to address this by identifying sets of genes or biological pathways contributing to a phenotype, through gene-gene interactions or other mechanisms, which are not the focus of conventional association methods. Results: Approaches that utilize GSEA can now take input from array chips, either gene-centric or genome-wide, but are highly sensitive to study design, SNP selection and pruning strategies, SNP-to-gene mapping, and pathway definitions. Here, we present lessons learned from our experience with GSEA of heart failure, a particularly challenging phenotype due to its underlying heterogeneous etiology. Conclusions: This case study shows that proper data handling is essential to avoid false-positive results. Well-defined pipelines for quality control are needed to avoid reporting spurious results using GSEA. Electronic supplementary material: The online version of this article (doi:10.1186/s13040-017-0137-5) contains supplementary material, which is available to authorized users. Twit Text FOAVip Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype ????BioData Min http://www.kidney.de/pget.php?&tw=1&pmid=28559929 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28559929 #FOAvip English Wikipedia <ref>{{Cite pmid|28559929}}</ref> Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype #MMPMID28559929 Tragante V; Gho JMIH; Felix JF; Vasan RS; Smith NL; Voight BF; Palmer C; van der Harst P; Moore JH; Asselbergs FW BioData Min 2017[]; 10 (ä): ä PMID28559929show ga Background: Genetic studies for complex diseases have predominantly discovered main effects at individual loci, but have not focused on genomic and environmental contexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims to address this by identifying sets of genes or biological pathways contributing to a phenotype, through gene-gene interactions or other mechanisms, which are not the focus of conventional association methods. Results: Approaches that utilize GSEA can now take input from array chips, either gene-centric or genome-wide, but are highly sensitive to study design, SNP selection and pruning strategies, SNP-to-gene mapping, and pathway definitions. Here, we present lessons learned from our experience with GSEA of heart failure, a particularly challenging phenotype due to its underlying heterogeneous etiology. Conclusions: This case study shows that proper data handling is essential to avoid false-positive results. Well-defined pipelines for quality control are needed to avoid reporting spurious results using GSEA. Electronic supplementary material: The online version of this article (doi:10.1186/s13040-017-0137-5) contains supplementary material, which is available to authorized users. äGene Set Enrichment Analyses: lessons learned from the heart failure p(epmc).pdf DeepDyve Pubget Overpricing