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10.1371/journal.pcbi.1005962 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1005962 C5831415!5831415!29447159     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 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 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 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 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Comput+Biol 2018 ; 14 (2): ä Nephropedia Template TP {{tp|p=29447159|t=2018. A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts |pdf=|usr=}}{{29447159}} gab.com Text A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=29447159 #FOAvip Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823?2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein?protein, disease?gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only. Twit Text FOAVip A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=29447159 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29447159 #FOAvip English Wikipedia <ref>{{Cite pmid|29447159}}</ref> A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts #MMPMID29447159 Westergaard D; Stærfeldt HH; Tønsberg C; Jensen LJ; Brunak S PLoS Comput Biol 2018[Feb]; 14 (2): ä PMID29447159show ga Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823?2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein?protein, disease?gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only. äA comprehensive and quantitative comparison of text-mining in 15 milli(epmc).pdf DeepDyve Pubget Overpricing