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10.1371/journal.pone.0149621 http://scihub22266oqcxt.onion/10.1371/journal.pone.0149621 C4769089!4769089!26919047     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2016 ; 11 (2): ä Nephropedia Template TP {{tp|p=26919047|t=2016. The Implicitome: A Resource for Rationalizing Gene-Disease Associations |pdf=|usr=}}{{26919047}} gab.com Text The Implicitome: A Resource for Rationalizing Gene-Disease Associations JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26919047 #FOAvip High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations. Twit Text FOAVip The Implicitome: A Resource for Rationalizing Gene-Disease Associations ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26919047 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26919047 #FOAvip English Wikipedia <ref>{{Cite pmid|26919047}}</ref> The Implicitome: A Resource for Rationalizing Gene-Disease Associations #MMPMID26919047 Hettne KM; Thompson M; van Haagen HHHBM; van der Horst E; Kaliyaperumal R; Mina E; Tatum Z; Laros JFJ; van Mulligen EM; Schuemie M; Aten E; Li TS; Bruskiewich R; Good BM; Su AI; Kors JA; den Dunnen J; van Ommen GJB; Roos M; ?t Hoen PA; Mons B; Schultes EA PLoS One 2016[]; 11 (2): ä PMID26919047show ga High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations. äThe Implicitome: A Resource for Rationalizing Gene-Disease Association(epmc).pdf DeepDyve Pubget Overpricing