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10.1093/bioinformatics/btv031 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btv031 C4443674!4443674!25617415     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2015 ; 31 (11): 1745-53 Nephropedia Template TP {{tp|p=25617415|t=2015. ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways |pdf=|usr=}}{{25617415}} gab.com Text ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25617415 #FOAvip Motivation: Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts?controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics.Results: Here, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens.Availability and implementation: Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.Contact: andreab@genetics.utah.edu or wej@bu.eduSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25617415 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25617415 #FOAvip English Wikipedia <ref>{{Cite pmid|25617415}}</ref> ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways #MMPMID25617415 Shen Y; Rahman M; Piccolo SR; Gusenleitner D; El-Chaar NN; Cheng L; Monti S; Bild AH; Johnson WE Bioinformatics 2015[Jun]; 31 (11): 1745-53 PMID25617415show ga Motivation: Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts?controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics.Results: Here, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens.Availability and implementation: Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.Contact: andreab@genetics.utah.edu or wej@bu.eduSupplementary information:Supplementary data are available at Bioinformatics online. äASSIGN: context-specific genomic profiling of multiple heterogeneous b(epmc).pdf DeepDyve Pubget Overpricing