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10.1155/2015/698527 http://scihub22266oqcxt.onion/10.1155/2015/698527 C4561867!4561867 !26380291     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26380291 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biomed+Res+Int 2015 ; 2015 (ä): 698527 Nephropedia Template TP {{tp|p=26380291 |t=2015. Supervised Learning Based Hypothesis Generation from Biomedical Literature |pdf=|usr=}}{{26380291 }} gab.com Text Supervised Learning Based Hypothesis Generation from Biomedical Literature JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26380291 #FOAvip Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system. Twit Text FOAVip Supervised Learning Based Hypothesis Generation from Biomedical Literature ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26380291 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26380291 #FOAvip English Wikipedia <ref>{{Cite pmid|26380291 }}</ref> Supervised Learning Based Hypothesis Generation from Biomedical Literature #MMPMID26380291 Sang S ; Yang Z ; Li Z ; Lin H Biomed Res Int 2015[]; 2015 (ä): 698527 PMID26380291 show ga Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system. |*Data Mining [MESH] |*Medical Informatics Computing [MESH] |*Publications [MESH] |Algorithms [MESH] |Humans [MESH]Supervised Learning Based Hypothesis Generation from Biomedical Litera(epmc).pdf DeepDyve Pubget Overpricing