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10.1155/2015/747156 http://scihub22266oqcxt.onion/10.1155/2015/747156 C4385608!4385608!25874227     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       Biomed+Res+Int 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=25874227|t=2015. RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases |pdf=|usr=}}{{25874227}} gab.com Text RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=25874227 #FOAvip High-throughput methods such as next-generation sequencing or DNA microarrays lack precision, as they return hundreds of genes for a single disease profile. Several computational methods applied to physical interaction of protein networks have been successfully used in identification of the best disease candidates for each expression profile. An open problem for these methods is the ability to combine and take advantage of the wealth of biomedical data publicly available. We propose an enhanced method to improve selection of the best disease targets for a multilayer biomedical network that integrates PPI data annotated with stable knowledge from OMIM diseases and GO biological processes. We present a comprehensive validation that demonstrates the advantage of the proposed approach, Recursive Random Walk with Restarts (RecRWR). The obtained results outline the superiority of the proposed approach, RecRWR, in identifying disease candidates, especially with high levels of biological noise and benefiting from all data available. Twit Text FOAVip RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=25874227 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25874227 #FOAvip English Wikipedia <ref>{{Cite pmid|25874227}}</ref> RecRWR: A Recursive Random Walk Method for Improved Identification of Diseases #MMPMID25874227 Perdiz Arrais J; Oliveira JL Biomed Res Int 2015[]; 2015 (ä): ä PMID25874227show ga High-throughput methods such as next-generation sequencing or DNA microarrays lack precision, as they return hundreds of genes for a single disease profile. Several computational methods applied to physical interaction of protein networks have been successfully used in identification of the best disease candidates for each expression profile. An open problem for these methods is the ability to combine and take advantage of the wealth of biomedical data publicly available. We propose an enhanced method to improve selection of the best disease targets for a multilayer biomedical network that integrates PPI data annotated with stable knowledge from OMIM diseases and GO biological processes. We present a comprehensive validation that demonstrates the advantage of the proposed approach, Recursive Random Walk with Restarts (RecRWR). The obtained results outline the superiority of the proposed approach, RecRWR, in identifying disease candidates, especially with high levels of biological noise and benefiting from all data available. äRecRWR: A Recursive Random Walk Method for Improved Identification of (epmc).pdf DeepDyve Pubget Overpricing