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10.1186/s13040-017-0141-9 http://scihub22266oqcxt.onion/10.1186/s13040-017-0141-9 C5501438!5501438!28694847     free     free     free 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 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       BioData+Min 2017 ; 10 (ä): ä Nephropedia Template TP {{tp|p=28694847|t=2017. Arete ? candidate gene prioritization using biological network topology with additional evidence types |pdf=|usr=}}{{28694847}} gab.com Text Arete candidate gene prioritization using biological network topology with additional evidence types JO: BioData Min http://www.kidney.de/pget.php?&tw=1&pmid=28694847 #FOAvip Background: Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data. Results: We have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method. The defining feature of the method is that both topological information of the biological networks and additional sources of evidence can be considered at the same time. The implementation was realized as an app extension for the Cytoscape graph analysis suite, and therefore can further benefit from the synergy with other analysis methods available as part of this system. Conclusions: We provide efficient reference implementations of two popular gene prioritization algorithms ? DIAMOnD and random walk with restart for the Cytoscape system. An extension of those methods was also developed that allows outputs of these algorithms to be combined with additional data. To demonstrate the utility of our software, we present two example disease gene prioritization application cases and show how our tool can be used to evaluate these different approaches. Electronic supplementary material: The online version of this article (doi:10.1186/s13040-017-0141-9) contains supplementary material, which is available to authorized users. Twit Text FOAVip Arete candidate gene prioritization using biological network topology with additional evidence types ????BioData Min http://www.kidney.de/pget.php?&tw=1&pmid=28694847 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28694847 #FOAvip English Wikipedia <ref>{{Cite pmid|28694847}}</ref> Arete ? candidate gene prioritization using biological network topology with additional evidence types #MMPMID28694847 Lysenko A; Boroevich KA; Tsunoda T BioData Min 2017[]; 10 (ä): ä PMID28694847show ga Background: Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data. Results: We have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method. The defining feature of the method is that both topological information of the biological networks and additional sources of evidence can be considered at the same time. The implementation was realized as an app extension for the Cytoscape graph analysis suite, and therefore can further benefit from the synergy with other analysis methods available as part of this system. Conclusions: We provide efficient reference implementations of two popular gene prioritization algorithms ? DIAMOnD and random walk with restart for the Cytoscape system. An extension of those methods was also developed that allows outputs of these algorithms to be combined with additional data. To demonstrate the utility of our software, we present two example disease gene prioritization application cases and show how our tool can be used to evaluate these different approaches. Electronic supplementary material: The online version of this article (doi:10.1186/s13040-017-0141-9) contains supplementary material, which is available to authorized users. äArete ? candidate gene prioritization using biological network topolog(epmc).pdf DeepDyve Pubget Overpricing