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10.1093/bioinformatics/btv237 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btv237 C4765864!4765864!26072501     free     free     free 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 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 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       Bioinformatics 2015 ; 31 (12): i339-47 Nephropedia Template TP {{tp|p=26072501|t=2015. MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence |pdf=|usr=}}{{26072501}} gab.com Text MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26072501 #FOAvip Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation.Methods: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using ?learning to rank?. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy.Results: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI.Availability and implementation: The software is available upon request.Contact:zhusf@fudan.edu.cn Twit Text FOAVip MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=26072501 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26072501 #FOAvip English Wikipedia <ref>{{Cite pmid|26072501}}</ref> MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence #MMPMID26072501 Liu K; Peng S; Wu J; Zhai C; Mamitsuka H; Zhu S Bioinformatics 2015[Jun]; 31 (12): i339-47 PMID26072501show ga Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation.Methods: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using ?learning to rank?. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy.Results: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI.Availability and implementation: The software is available upon request.Contact:zhusf@fudan.edu.cn äMeSHLabeler: improving the accuracy of large-scale MeSH indexing by in(epmc).pdf DeepDyve Pubget Overpricing