Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1186/s12859-016-1041-6 http://scihub22266oqcxt.onion/10.1186/s12859-016-1041-6 C4905607!4905607!27295093     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       BMC+Bioinformatics 2016 ; 17 (Suppl 5): ä Nephropedia Template TP {{tp|p=27295093|t=2016. DrugQuest - a text mining workflow for drug association discovery |pdf=|usr=}}{{27295093}} gab.com Text DrugQuest - a text mining workflow for drug association discovery JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27295093 #FOAvip Background: Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Results: Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank ?Description?, ?Indication?, ?Pharmacodynamics? and ?Mechanism of Action? text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. Conclusions: DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest. Twit Text FOAVip DrugQuest - a text mining workflow for drug association discovery ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27295093 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27295093 #FOAvip English Wikipedia <ref>{{Cite pmid|27295093}}</ref> DrugQuest - a text mining workflow for drug association discovery #MMPMID27295093 Papanikolaou N; Pavlopoulos GA; Theodosiou T; Vizirianakis IS; Iliopoulos I BMC Bioinformatics 2016[]; 17 (Suppl 5): ä PMID27295093show ga Background: Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Results: Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank ?Description?, ?Indication?, ?Pharmacodynamics? and ?Mechanism of Action? text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. Conclusions: DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest. äDrugQuest - a text mining workflow for drug association discovery (epmc).pdf DeepDyve Pubget Overpricing