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10.1007/978-1-62703-658-0_11 http://scihub22266oqcxt.onion/10.1007/978-1-62703-658-0_11 C4676806!4676806!24061923     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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Methods+Mol+Biol 2014 ; 1084 (ä): 193-226 Nephropedia Template TP {{tp|p=24061923|t=2014. Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins |pdf=|usr=}}{{24061923}} gab.com Text Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins JO: Methods Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=24061923 #FOAvip It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided. Twit Text FOAVip Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins ????Methods Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=24061923 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24061923 #FOAvip English Wikipedia <ref>{{Cite pmid|24061923}}</ref> Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins #MMPMID24061923 David CC; Jacobs DJ Methods Mol Biol 2014[]; 1084 (ä): 193-226 PMID24061923show ga It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided. äPrincipal Component Analysis: A Method for Determining the Essential D(epmc).pdf DeepDyve Pubget Overpricing