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10.1111/biom.12374 http://scihub22266oqcxt.onion/10.1111/biom.12374 C4760921!4760921!26288029     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrics 2016 ; 72 (1): 85-94 Nephropedia Template TP {{tp|p=26288029|t=2016. Detection of Gene-Gene Interactions using Multistage Sparse and Low-Rank Regression |pdf=|usr=}}{{26288029}} gab.com Text Detection of Gene-Gene Interactions using Multistage Sparse and Low-Rank Regression JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=26288029 #FOAvip Finding an efficient and computationally feasible approach to deal with the curse of high-dimensionality is a daunting challenge faced by modern biological science. The problem becomes even more severe when the interactions are the research focus. To improve the performance of statistical analyses, we propose a sparse and low-rank (SLR) screening based on the combination of a low-rank interaction model and the Lasso screening. SLR models the interaction effects using a low-rank matrix to achieve parsimonious parametrization. The low-rank model increases the efficiency of statistical inference and, hence, SLR screening is able to more accurately detect gene-gene interactions than conventional methods. Incorporation of SLR screening into the Screen-and-Clean approach (Wasserman and Roeder, 2009; Wu et al., 2010) is also discussed, which suffers less penalty from Boferroni correction, and is able to assign p-values for the identified variables in high dimensional model. We apply the proposed screening procedure to the Warfarin dosage study and the CoLaus study. The results suggest that the new procedure can identify main and interaction effects that would have been omitted by conventional screening methods. Twit Text FOAVip Detection of Gene-Gene Interactions using Multistage Sparse and Low-Rank Regression ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=26288029 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26288029 #FOAvip English Wikipedia <ref>{{Cite pmid|26288029}}</ref> Detection of Gene-Gene Interactions using Multistage Sparse and Low-Rank Regression #MMPMID26288029 Hung H; Lin YT; Chen P; Wang CC; Huang SY; Tzeng JY Biometrics 2016[Mar]; 72 (1): 85-94 PMID26288029show ga Finding an efficient and computationally feasible approach to deal with the curse of high-dimensionality is a daunting challenge faced by modern biological science. The problem becomes even more severe when the interactions are the research focus. To improve the performance of statistical analyses, we propose a sparse and low-rank (SLR) screening based on the combination of a low-rank interaction model and the Lasso screening. SLR models the interaction effects using a low-rank matrix to achieve parsimonious parametrization. The low-rank model increases the efficiency of statistical inference and, hence, SLR screening is able to more accurately detect gene-gene interactions than conventional methods. Incorporation of SLR screening into the Screen-and-Clean approach (Wasserman and Roeder, 2009; Wu et al., 2010) is also discussed, which suffers less penalty from Boferroni correction, and is able to assign p-values for the identified variables in high dimensional model. We apply the proposed screening procedure to the Warfarin dosage study and the CoLaus study. The results suggest that the new procedure can identify main and interaction effects that would have been omitted by conventional screening methods. äDetection of Gene-Gene Interactions using Multistage Sparse and Low-Ra(epmc).pdf DeepDyve Pubget Overpricing