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10.3390/microarrays3010039 http://scihub22266oqcxt.onion/10.3390/microarrays3010039 C5003454!5003454!27605029     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 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Microarrays+(Basel) 2014 ; 3 (1): 39-51 Nephropedia Template TP {{tp|p=27605029|t=2014. Comparative Analysis of Biologically Relevant Response Curves in Gene Expression Experiments: Heteromorphy, Heterochrony, and Heterometry |pdf=|usr=}}{{27605029}} gab.com Text Comparative Analysis of Biologically Relevant Response Curves in Gene Expression Experiments: Heteromorphy, Heterochrony, and Heterometry JO: Microarrays (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=27605029 #FOAvip To gain biological insights, investigators sometimes compare sequences of gene expression measurements under two scenarios (such as two drugs or species). For this situation, we developed an algorithm to fit, identify, and compare biologically relevant response curves in terms of heteromorphy (different curves), heterochrony (different transition times), and heterometry (different magnitudes). The curves are flat, linear, sigmoid, hockey-stick (sigmoid missing a steady state), transient (sigmoid missing two steady states), impulse (with peak or trough), step (with intermediate-level plateau), impulse+ (impulse with an extra parameter), step+ (step with an extra parameter), further characterized by upward or downward trend. To reduce overfitting, we fit the curves to every other response, evaluated the fit in the remaining responses, and identified the most parsimonious curves that yielded a good fit. We measured goodness of fit using a statistic comparable over different genes, namely the square root of the mean squared prediction error as a percentage of the range of responses, which we call the relative prediction error (RPE). We illustrated the algorithm using data on gene expression at 14 times in the embryonic development in two species of frogs. Software written in Mathematica is freely available. Twit Text FOAVip Comparative Analysis of Biologically Relevant Response Curves in Gene Expression Experiments: Heteromorphy, Heterochrony, and Heterometry ????Microarrays (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=27605029 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27605029 #FOAvip English Wikipedia <ref>{{Cite pmid|27605029}}</ref> Comparative Analysis of Biologically Relevant Response Curves in Gene Expression Experiments: Heteromorphy, Heterochrony, and Heterometry #MMPMID27605029 Baker SG Microarrays (Basel) 2014[Mar]; 3 (1): 39-51 PMID27605029show ga To gain biological insights, investigators sometimes compare sequences of gene expression measurements under two scenarios (such as two drugs or species). For this situation, we developed an algorithm to fit, identify, and compare biologically relevant response curves in terms of heteromorphy (different curves), heterochrony (different transition times), and heterometry (different magnitudes). The curves are flat, linear, sigmoid, hockey-stick (sigmoid missing a steady state), transient (sigmoid missing two steady states), impulse (with peak or trough), step (with intermediate-level plateau), impulse+ (impulse with an extra parameter), step+ (step with an extra parameter), further characterized by upward or downward trend. To reduce overfitting, we fit the curves to every other response, evaluated the fit in the remaining responses, and identified the most parsimonious curves that yielded a good fit. We measured goodness of fit using a statistic comparable over different genes, namely the square root of the mean squared prediction error as a percentage of the range of responses, which we call the relative prediction error (RPE). We illustrated the algorithm using data on gene expression at 14 times in the embryonic development in two species of frogs. Software written in Mathematica is freely available. äComparative Analysis of Biologically Relevant Response Curves in Gene (epmc).pdf DeepDyve Pubget Overpricing