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10.1093/database/baw079 http://scihub22266oqcxt.onion/10.1093/database/baw079 C4865361!4865361 !27173525     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27173525 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27173525 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Database+(Oxford) 2016 ; 2016 (ä): ä Nephropedia Template TP {{tp|p=27173525 |t=2016. BELTracker: evidence sentence retrieval for BEL statements |pdf=|usr=}}{{27173525 }} gab.com Text BELTracker: evidence sentence retrieval for BEL statements JO: Database (Oxford) http://www.kidney.de/pget.php?&tw=1&pmid=27173525 #FOAvip Biological expression language (BEL) is one of the main formal representation models of biological networks. The primary source of information for curating biological networks in BEL representation has been literature. It remains a challenge to identify relevant articles and the corresponding evidence statements for curating and validating BEL statements. In this paper, we describe BELTracker, a tool used to retrieve and rank evidence sentences from PubMed abstracts and full-text articles for a given BEL statement (per the 2015 task requirements of BioCreative V BEL Task). The system is comprised of three main components, (i) translation of a given BEL statement to an information retrieval (IR) query, (ii) retrieval of relevant PubMed citations and (iii) finding and ranking the evidence sentences in those citations. BELTracker uses a combination of multiple approaches based on traditional IR, machine learning, and heuristics to accomplish the task. The system identified and ranked at least one fully relevant evidence sentence in the top 10 retrieved sentences for 72 out of 97 BEL statements in the test set. BELTracker achieved a precision of 0.392, 0.532 and 0.615 when evaluated with three criteria, namely full, relaxed and context criteria, respectively, by the task organizers. Our team at Mayo Clinic was the only participant in this task. BELTracker is available as a RESTful API and is available for public use.Database URL: http://www.openbionlp.org:8080/BelTracker/finder/Given_BEL_Statement. Twit Text FOAVip BELTracker: evidence sentence retrieval for BEL statements ????Database (Oxford) http://www.kidney.de/pget.php?&tw=1&pmid=27173525 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27173525 #FOAvip English Wikipedia <ref>{{Cite pmid|27173525 }}</ref> BELTracker: evidence sentence retrieval for BEL statements #MMPMID27173525 Rastegar-Mojarad M ; Komandur Elayavilli R ; Liu H Database (Oxford) 2016[]; 2016 (ä): ä PMID27173525 show ga Biological expression language (BEL) is one of the main formal representation models of biological networks. The primary source of information for curating biological networks in BEL representation has been literature. It remains a challenge to identify relevant articles and the corresponding evidence statements for curating and validating BEL statements. In this paper, we describe BELTracker, a tool used to retrieve and rank evidence sentences from PubMed abstracts and full-text articles for a given BEL statement (per the 2015 task requirements of BioCreative V BEL Task). The system is comprised of three main components, (i) translation of a given BEL statement to an information retrieval (IR) query, (ii) retrieval of relevant PubMed citations and (iii) finding and ranking the evidence sentences in those citations. BELTracker uses a combination of multiple approaches based on traditional IR, machine learning, and heuristics to accomplish the task. The system identified and ranked at least one fully relevant evidence sentence in the top 10 retrieved sentences for 72 out of 97 BEL statements in the test set. BELTracker achieved a precision of 0.392, 0.532 and 0.615 when evaluated with three criteria, namely full, relaxed and context criteria, respectively, by the task organizers. Our team at Mayo Clinic was the only participant in this task. BELTracker is available as a RESTful API and is available for public use.Database URL: http://www.openbionlp.org:8080/BelTracker/finder/Given_BEL_Statement. |*Internet [MESH] |*Natural Language Processing [MESH] |*Software [MESH] |Computational Biology/*methods [MESH] |Data Curation [MESH] |Data Mining/*methods [MESH]BELTracker: evidence sentence retrieval for BEL statements (epmc).pdf DeepDyve Pubget Overpricing