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10.1186/s12859-017-1712-y http://scihub22266oqcxt.onion/10.1186/s12859-017-1712-y C5481992!5481992!28645323     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Bioinformatics 2017 ; 18 (ä): ä Nephropedia Template TP {{tp|p=28645323|t=2017. Early response index: a statistic to discover potential early stage disease biomarkers |pdf=|usr=}}{{28645323}} gab.com Text Early response index: a statistic to discover potential early stage disease biomarkers JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28645323 #FOAvip Background: Identifying disease correlated features early before large number of molecules are impacted by disease progression with significant abundance change is very advantageous to biologists for developing early disease diagnosis biomarkers. Disease correlated features have relatively low level of abundance change at early stages. Finding them using existing bioinformatic tools in high throughput data is a challenging task since the technology suffers from limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect molecules with high abundance changes, frequently missing early disease diagnostic markers. Results: We present a new statistic called early response index (ERI) to prioritize disease correlated molecules as potential early biomarkers. Instead of classification accuracy, ERI measures the average classification accuracy improvement attainable by a feature when it is united with other counterparts for classification. ERI is more sensitive to abundance changes than other ranking statistics. We have shown that ERI significantly outperforms SAM and Localfdr in detecting early responding molecules in a proteomics study of a mouse model of multiple sclerosis. Importantly, ERI was able to detect many disease relevant proteins before those algorithms detect them at a later time point. Conclusions: ERI method is more sensitive for significant feature detection during early stage of disease development. It potentially has a higher specificity for biomarker discovery, and can be used to identify critical time frame for disease intervention. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-017-1712-y) contains supplementary material, which is available to authorized users. Twit Text FOAVip Early response index: a statistic to discover potential early stage disease biomarkers ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28645323 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28645323 #FOAvip English Wikipedia <ref>{{Cite pmid|28645323}}</ref> Early response index: a statistic to discover potential early stage disease biomarkers #MMPMID28645323 Salekin S; Bari MG; Raphael I; Forsthuber TG; Zhang J( BMC Bioinformatics 2017[]; 18 (ä): ä PMID28645323show ga Background: Identifying disease correlated features early before large number of molecules are impacted by disease progression with significant abundance change is very advantageous to biologists for developing early disease diagnosis biomarkers. Disease correlated features have relatively low level of abundance change at early stages. Finding them using existing bioinformatic tools in high throughput data is a challenging task since the technology suffers from limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect molecules with high abundance changes, frequently missing early disease diagnostic markers. Results: We present a new statistic called early response index (ERI) to prioritize disease correlated molecules as potential early biomarkers. Instead of classification accuracy, ERI measures the average classification accuracy improvement attainable by a feature when it is united with other counterparts for classification. ERI is more sensitive to abundance changes than other ranking statistics. We have shown that ERI significantly outperforms SAM and Localfdr in detecting early responding molecules in a proteomics study of a mouse model of multiple sclerosis. Importantly, ERI was able to detect many disease relevant proteins before those algorithms detect them at a later time point. Conclusions: ERI method is more sensitive for significant feature detection during early stage of disease development. It potentially has a higher specificity for biomarker discovery, and can be used to identify critical time frame for disease intervention. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-017-1712-y) contains supplementary material, which is available to authorized users. äEarly response index: a statistic to discover potential early stage di(epmc).pdf DeepDyve Pubget Overpricing