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10.1186/s13326-015-0026-0 http://scihub22266oqcxt.onion/10.1186/s13326-015-0026-0 C4485340!4485340!26131352     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       J+Biomed+Semantics 2015 ; 6 (ä): ä Nephropedia Template TP {{tp|p=26131352|t=2015. Ambiguity and variability of database and software names in bioinformatics |pdf=|usr=}}{{26131352}} gab.com Text Ambiguity and variability of database and software names in bioinformatics JO: J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=26131352 #FOAvip Background: There are numerous options available to achieve various tasks in bioinformatics, but until recently, there were no tools that could systematically identify mentions of databases and tools within the literature. In this paper we explore the variability and ambiguity of database and software name mentions and compare dictionary and machine learning approaches to their identification. Results: Through the development and analysis of a corpus of 60 full-text documents manually annotated at the mention level, we report high variability and ambiguity in database and software mentions. On a test set of 25 full-text documents, a baseline dictionary look-up achieved an F-score of 46 %, highlighting not only variability and ambiguity but also the extensive number of new resources introduced. A machine learning approach achieved an F-score of 63 % (with precision of 74 %) and 70 % (with precision of 83 %) for strict and lenient matching respectively. We characterise the issues with various mention types and propose potential ways of capturing additional database and software mentions in the literature. Conclusions: Our analyses show that identification of mentions of databases and tools is a challenging task that cannot be achieved by relying on current manually-curated resource repositories. Although machine learning shows improvement and promise (primarily in precision), more contextual information needs to be taken into account to achieve a good degree of accuracy. Twit Text FOAVip Ambiguity and variability of database and software names in bioinformatics ????J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=26131352 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26131352 #FOAvip English Wikipedia <ref>{{Cite pmid|26131352}}</ref> Ambiguity and variability of database and software names in bioinformatics #MMPMID26131352 Duck G; Kovacevic A; Robertson DL; Stevens R; Nenadic G J Biomed Semantics 2015[]; 6 (ä): ä PMID26131352show ga Background: There are numerous options available to achieve various tasks in bioinformatics, but until recently, there were no tools that could systematically identify mentions of databases and tools within the literature. In this paper we explore the variability and ambiguity of database and software name mentions and compare dictionary and machine learning approaches to their identification. Results: Through the development and analysis of a corpus of 60 full-text documents manually annotated at the mention level, we report high variability and ambiguity in database and software mentions. On a test set of 25 full-text documents, a baseline dictionary look-up achieved an F-score of 46 %, highlighting not only variability and ambiguity but also the extensive number of new resources introduced. A machine learning approach achieved an F-score of 63 % (with precision of 74 %) and 70 % (with precision of 83 %) for strict and lenient matching respectively. We characterise the issues with various mention types and propose potential ways of capturing additional database and software mentions in the literature. Conclusions: Our analyses show that identification of mentions of databases and tools is a challenging task that cannot be achieved by relying on current manually-curated resource repositories. Although machine learning shows improvement and promise (primarily in precision), more contextual information needs to be taken into account to achieve a good degree of accuracy. äAmbiguity and variability of database and software names in bioinforma(epmc).pdf DeepDyve Pubget Overpricing