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10.1111/biom.12467 http://scihub22266oqcxt.onion/10.1111/biom.12467 C4975686!4975686!26849781     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrics 2016 ; 72 (3): 791-803 Nephropedia Template TP {{tp|p=26849781|t=2016. Estimation of Sparse Directed Acyclic Graphs for Multivariate Counts Data |pdf=|usr=}}{{26849781}} gab.com Text Estimation of Sparse Directed Acyclic Graphs for Multivariate Counts Data JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=26849781 #FOAvip The next-generation sequencing data, called high throughput sequencing data, are recorded as count data, which is generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this paper provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study. Twit Text FOAVip Estimation of Sparse Directed Acyclic Graphs for Multivariate Counts Data ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=26849781 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26849781 #FOAvip English Wikipedia <ref>{{Cite pmid|26849781}}</ref> Estimation of Sparse Directed Acyclic Graphs for Multivariate Counts Data #MMPMID26849781 Han SW; Zhong H Biometrics 2016[Sep]; 72 (3): 791-803 PMID26849781show ga The next-generation sequencing data, called high throughput sequencing data, are recorded as count data, which is generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this paper provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study. äEstimation of Sparse Directed Acyclic Graphs for Multivariate Counts D(epmc).pdf DeepDyve Pubget Overpricing