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10.1186/s13326-015-0021-5 http://scihub22266oqcxt.onion/10.1186/s13326-015-0021-5 C4492092!4492092!26150906     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biomed+Semantics 2015 ; 6 (ä): ä Nephropedia Template TP {{tp|p=26150906|t=2015. Discovering relations between indirectly connected biomedical concepts |pdf=|usr=}}{{26150906}} gab.com Text Discovering relations between indirectly connected biomedical concepts JO: J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=26150906 #FOAvip Background: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation. Results: It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely ?has target?, and ?may treat?, are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach. Conclusions: Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts. Electronic supplementary material: The online version of this article (doi:10.1186/s13326-015-0021-5) contains supplementary material, which is available to authorized users. Twit Text FOAVip Discovering relations between indirectly connected biomedical concepts ????J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=26150906 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26150906 #FOAvip English Wikipedia <ref>{{Cite pmid|26150906}}</ref> Discovering relations between indirectly connected biomedical concepts #MMPMID26150906 Weissenborn D; Schroeder M; Tsatsaronis G J Biomed Semantics 2015[]; 6 (ä): ä PMID26150906show ga Background: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation. Results: It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely ?has target?, and ?may treat?, are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach. Conclusions: Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts. Electronic supplementary material: The online version of this article (doi:10.1186/s13326-015-0021-5) contains supplementary material, which is available to authorized users. äDiscovering relations between indirectly connected biomedical concepts(epmc).pdf DeepDyve Pubget Overpricing