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10.1155/2015/916352 http://scihub22266oqcxt.onion/10.1155/2015/916352 C4539434!4539434!26346000     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26346000.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biomed+Res+Int 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=26346000|t=2015. Dynamic Model for RNA-seq Data Analysis |pdf=|usr=}}{{26346000}} gab.com Text Dynamic Model for RNA-seq Data Analysis JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26346000 #FOAvip By measuring messenger RNA levels for all genes in a sample, RNA-seq provides an attractive option to characterize the global changes in transcription. RNA-seq is becoming the widely used platform for gene expression profiling. However, real transcription signals in the RNA-seq data are confounded with measurement and sequencing errors and other random biological/technical variation. To extract biologically useful transcription process from the RNA-seq data, we propose to use the second ODE for modeling the RNA-seq data. We use differential principal analysis to develop statistical methods for estimation of location-varying coefficients of the ODE. We validate the accuracy of the ODE model to fit the RNA-seq data by prediction analysis and 5-fold cross validation. To further evaluate the performance of the ODE model for RNA-seq data analysis, we used the location-varying coefficients of the second ODE as features to classify the normal and tumor cells. We demonstrate that even using the ODE model for single gene we can achieve high classification accuracy. We also conduct response analysis to investigate how the transcription process responds to the perturbation of the external signals and identify dozens of genes that are related to cancer. Twit Text FOAVip Dynamic Model for RNA-seq Data Analysis ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26346000 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26346000 #FOAvip English Wikipedia <ref>{{Cite pmid|26346000}}</ref> Dynamic Model for RNA-seq Data Analysis #MMPMID26346000 Li L; Xiong M Biomed Res Int 2015[]; 2015 (ä): ä PMID26346000show ga By measuring messenger RNA levels for all genes in a sample, RNA-seq provides an attractive option to characterize the global changes in transcription. RNA-seq is becoming the widely used platform for gene expression profiling. However, real transcription signals in the RNA-seq data are confounded with measurement and sequencing errors and other random biological/technical variation. To extract biologically useful transcription process from the RNA-seq data, we propose to use the second ODE for modeling the RNA-seq data. We use differential principal analysis to develop statistical methods for estimation of location-varying coefficients of the ODE. We validate the accuracy of the ODE model to fit the RNA-seq data by prediction analysis and 5-fold cross validation. To further evaluate the performance of the ODE model for RNA-seq data analysis, we used the location-varying coefficients of the second ODE as features to classify the normal and tumor cells. We demonstrate that even using the ODE model for single gene we can achieve high classification accuracy. We also conduct response analysis to investigate how the transcription process responds to the perturbation of the external signals and identify dozens of genes that are related to cancer. äDynamic Model for RNA-seq Data Analysis (epmc).pdf DeepDyve Pubget Overpricing