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10.1080/17460441.2021.1910673 http://scihub22266oqcxt.onion/10.1080/17460441.2021.1910673 33843398!ä!33843398 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33843398&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 217.6 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\33843398.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Expert+Opin+Drug+Discov 2021 ; 16 (9): 1057-1069 Nephropedia Template TP {{tp|p=33843398|t=2021. Knowledge graphs and their applications in drug discovery |pdf=|usr=}}{{33843398}} gab.com Text Knowledge graphs and their applications in drug discovery JO: Expert Opin Drug Discov http://www.kidney.de/pget.php?&tw=1&pmid=33843398 #FOAvip INTRODUCTION: Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. AREAS COVERED: In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies. EXPERT OPINION: Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery. Twit Text FOAVip Knowledge graphs and their applications in drug discovery ????Expert Opin Drug Discov http://www.kidney.de/pget.php?&tw=1&pmid=33843398 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33843398 #FOAvip English Wikipedia <ref>{{Cite pmid|33843398}}</ref> Knowledge graphs and their applications in drug discovery #MMPMID33843398 MacLean F Expert Opin Drug Discov 2021[Sep]; 16 (9): 1057-1069 PMID33843398show ga INTRODUCTION: Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. AREAS COVERED: In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies. EXPERT OPINION: Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery. |*Computer Graphics[MESH] |*Machine Learning[MESH] |Algorithms[MESH] |COVID-19 Drug Treatment[MESH] |Drug Discovery/*methods[MESH] |Drug Repositioning/methods[MESH] |Humans[MESH]Knowledge graphs and their applications in drug discovery (epmc).pdf DeepDyve Pubget Overpricing