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10.1186/s12859-018-2167-5 http://scihub22266oqcxt.onion/10.1186/s12859-018-2167-5 C5975655!5975655!29843590     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29843590&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 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 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 2018 ; 19 (ä): ä Nephropedia Template TP {{tp|p=29843590|t=2018. SemaTyP: a knowledge graph based literature mining method for drug discovery |pdf=|usr=}}{{29843590}} gab.com Text SemaTyP: a knowledge graph based literature mining method for drug discovery JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29843590 #FOAvip Background: Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Methods: Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies? existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. Results: The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. Conclusions: In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods. Electronic supplementary material: The online version of this article (10.1186/s12859-018-2167-5) contains supplementary material, which is available to authorized users. Twit Text FOAVip SemaTyP: a knowledge graph based literature mining method for drug discovery ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29843590 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29843590 #FOAvip English Wikipedia <ref>{{Cite pmid|29843590}}</ref> SemaTyP: a knowledge graph based literature mining method for drug discovery #MMPMID29843590 Sang S; Yang Z; Wang L; Liu X; Lin H; Wang J BMC Bioinformatics 2018[]; 19 (ä): ä PMID29843590show ga Background: Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Methods: Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies? existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. Results: The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. Conclusions: In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods. Electronic supplementary material: The online version of this article (10.1186/s12859-018-2167-5) contains supplementary material, which is available to authorized users. äSemaTyP: a knowledge graph based literature mining method for drug dis(epmc).pdf DeepDyve Pubget Overpricing