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10.1214/16-aoas990 http://scihub22266oqcxt.onion/10.1214/16-aoas990 C5388190!5388190 !28408966     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28408966 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28408966 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Ann+Appl+Stat 2017 ; 11 (1 ): 41-68 Nephropedia Template TP {{tp|p=28408966 |t=2017. Gene Network Reconstruction using Global-Local Shrinkage Priors |pdf=|usr=}}{{28408966 }} gab.com Text Gene Network Reconstruction using Global-Local Shrinkage Priors JO: Ann Appl Stat http://www.kidney.de/pget.php?&tw=1&pmid=28408966 #FOAvip Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighbourhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference. We employ a simple Bayesian model with non-sparse, conjugate priors to facilitate the use of fast variational approximations to posteriors. We discuss empirical Bayes estimation of hyper-parameters of the priors, and propose a novel approach to rank-based posterior thresholding. Using extensive model- and data-based simulations, we demonstrate that the proposed inference strategy outperforms popular (sparse) methods, yields more stable edges, and is more reproducible. The proposed method, termed ShrinkNet, is then applied to Glioblastoma to investigate the interactions between genes associated with patient survival. Twit Text FOAVip Gene Network Reconstruction using Global-Local Shrinkage Priors ????Ann Appl Stat http://www.kidney.de/pget.php?&tw=1&pmid=28408966 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28408966 #FOAvip English Wikipedia <ref>{{Cite pmid|28408966 }}</ref> Gene Network Reconstruction using Global-Local Shrinkage Priors #MMPMID28408966 Leday GG ; de Gunst MC ; Kpogbezan GB ; van der Vaart AW ; van Wieringen WN ; van de Wiel MA Ann Appl Stat 2017[Mar]; 11 (1 ): 41-68 PMID28408966 show ga Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighbourhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference. We employ a simple Bayesian model with non-sparse, conjugate priors to facilitate the use of fast variational approximations to posteriors. We discuss empirical Bayes estimation of hyper-parameters of the priors, and propose a novel approach to rank-based posterior thresholding. Using extensive model- and data-based simulations, we demonstrate that the proposed inference strategy outperforms popular (sparse) methods, yields more stable edges, and is more reproducible. The proposed method, termed ShrinkNet, is then applied to Glioblastoma to investigate the interactions between genes associated with patient survival. äGene Network Reconstruction using Global-Local Shrinkage Priors (epmc).pdf DeepDyve Pubget Overpricing