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10.1186/s12859-018-2106-5 http://scihub22266oqcxt.onion/10.1186/s12859-018-2106-5 C5869792!5869792!29587646     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Bioinformatics 2018 ; 19 (ä): ä Nephropedia Template TP {{tp|p=29587646|t=2018. A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets |pdf=|usr=}}{{29587646}} gab.com Text A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29587646 #FOAvip Background: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding. Results: We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions. Conclusions: The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO. Electronic supplementary material: The online version of this article (10.1186/s12859-018-2106-5) contains supplementary material, which is available to authorized users. Twit Text FOAVip A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29587646 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29587646 #FOAvip English Wikipedia <ref>{{Cite pmid|29587646}}</ref> A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets #MMPMID29587646 Liang K; Du C; You H; Nettleton D BMC Bioinformatics 2018[]; 19 (ä): ä PMID29587646show ga Background: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding. Results: We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions. Conclusions: The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO. Electronic supplementary material: The online version of this article (10.1186/s12859-018-2106-5) contains supplementary material, which is available to authorized users. äA hidden Markov tree model for testing multiple hypotheses correspondi(epmc).pdf DeepDyve Pubget Overpricing