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10.1038/srep33521 http://scihub22266oqcxt.onion/10.1038/srep33521 C5027599!5027599!27641933     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 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\27641933.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2016 ; 6 (ä): ä Nephropedia Template TP {{tp|p=27641933|t=2016. Automatic Bayesian single molecule identification for localization microscopy |pdf=|usr=}}{{27641933}} gab.com Text Automatic Bayesian single molecule identification for localization microscopy JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27641933 #FOAvip Single molecule localization microscopy (SMLM) is on its way to become a mainstream imaging technique in the life sciences. However, analysis of SMLM data is biased by user provided subjective parameters required by the analysis software. To remove this human bias we introduce here the Auto-Bayes method that executes the analysis of SMLM data automatically. We demonstrate the success of the method using the photoelectron count of an emitter as selection characteristic. Moreover, the principle can be used for any characteristic that is bimodally distributed with respect to false and true emitters. The method also allows generation of an emitter reliability map for estimating quality of SMLM-based structures. The potential of the Auto-Bayes method is shown by the fact that our first basic implementation was able to outperform all software packages that were compared in the ISBI online challenge in 2015, with respect to molecule detection (Jaccard index). Twit Text FOAVip Automatic Bayesian single molecule identification for localization microscopy ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27641933 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27641933 #FOAvip English Wikipedia <ref>{{Cite pmid|27641933}}</ref> Automatic Bayesian single molecule identification for localization microscopy #MMPMID27641933 Tang Y; Hendriks J; Gensch T; Dai L; Li J Sci Rep 2016[]; 6 (ä): ä PMID27641933show ga Single molecule localization microscopy (SMLM) is on its way to become a mainstream imaging technique in the life sciences. However, analysis of SMLM data is biased by user provided subjective parameters required by the analysis software. To remove this human bias we introduce here the Auto-Bayes method that executes the analysis of SMLM data automatically. We demonstrate the success of the method using the photoelectron count of an emitter as selection characteristic. Moreover, the principle can be used for any characteristic that is bimodally distributed with respect to false and true emitters. The method also allows generation of an emitter reliability map for estimating quality of SMLM-based structures. The potential of the Auto-Bayes method is shown by the fact that our first basic implementation was able to outperform all software packages that were compared in the ISBI online challenge in 2015, with respect to molecule detection (Jaccard index). äAutomatic Bayesian single molecule identification for localization mic(epmc).pdf DeepDyve Pubget Overpricing