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10.1093/bioinformatics/btu286 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu286 C4058950!4058950 !24931999     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24931999 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2014 ; 30 (12 ): i310-8 Nephropedia Template TP {{tp|p=24931999 |t=2014. Stochastic EM-based TFBS motif discovery with MITSU |pdf=|usr=}}{{24931999 }} gab.com Text Stochastic EM-based TFBS motif discovery with MITSU JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931999 #FOAvip MOTIVATION: The Expectation-Maximization (EM) algorithm has been successfully applied to the problem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used motif discovery algorithms. In the wider field of probabilistic modelling, the stochastic EM (sEM) algorithm has been used to overcome some of the limitations of the EM algorithm; however, the application of sEM to motif discovery has not been fully explored. RESULTS: We present MITSU (Motif discovery by ITerative Sampling and Updating), a novel algorithm for motif discovery, which combines sEM with an improved approximation to the likelihood function, which is unconstrained with regard to the distribution of motif occurrences within the input dataset. The algorithm is evaluated quantitatively on realistic synthetic data and several collections of characterized prokaryotic TFBS motifs and shown to outperform EM and an alternative sEM-based algorithm, particularly in terms of site-level positive predictive value. AVAILABILITY AND IMPLEMENTATION: Java executable available for download at http://www.sourceforge.net/p/mitsu-motif/, supported on Linux/OS X. Twit Text FOAVip Stochastic EM-based TFBS motif discovery with MITSU ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931999 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24931999 #FOAvip English Wikipedia <ref>{{Cite pmid|24931999 }}</ref> Stochastic EM-based TFBS motif discovery with MITSU #MMPMID24931999 Kilpatrick AM ; Ward B ; Aitken S Bioinformatics 2014[Jun]; 30 (12 ): i310-8 PMID24931999 show ga MOTIVATION: The Expectation-Maximization (EM) algorithm has been successfully applied to the problem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used motif discovery algorithms. In the wider field of probabilistic modelling, the stochastic EM (sEM) algorithm has been used to overcome some of the limitations of the EM algorithm; however, the application of sEM to motif discovery has not been fully explored. RESULTS: We present MITSU (Motif discovery by ITerative Sampling and Updating), a novel algorithm for motif discovery, which combines sEM with an improved approximation to the likelihood function, which is unconstrained with regard to the distribution of motif occurrences within the input dataset. The algorithm is evaluated quantitatively on realistic synthetic data and several collections of characterized prokaryotic TFBS motifs and shown to outperform EM and an alternative sEM-based algorithm, particularly in terms of site-level positive predictive value. AVAILABILITY AND IMPLEMENTATION: Java executable available for download at http://www.sourceforge.net/p/mitsu-motif/, supported on Linux/OS X. |*Algorithms [MESH] |*Regulatory Elements, Transcriptional [MESH] |Binding Sites [MESH] |Escherichia coli/genetics [MESH] |Humans [MESH] |Models, Statistical [MESH] |Nucleotide Motifs [MESH] |Sequence Analysis, DNA/*methods [MESH] |Stochastic Processes [MESH]Stochastic EM-based TFBS motif discovery with MITSU (epmc).pdf DeepDyve Pubget Overpricing