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10.1038/s41467-017-00680-8 http://scihub22266oqcxt.onion/10.1038/s41467-017-00680-8 C5603535!5603535 !28924171     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 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\28924171 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Nat+Commun 2017 ; 8 (1 ): 573 Nephropedia Template TP {{tp|p=28924171 |t=2017. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information |pdf=|usr=}}{{28924171 }} gab.com Text A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=28924171 #FOAvip The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations. Twit Text FOAVip A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=28924171 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28924171 #FOAvip English Wikipedia <ref>{{Cite pmid|28924171 }}</ref> A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information #MMPMID28924171 Luo Y ; Zhao X ; Zhou J ; Yang J ; Zhang Y ; Kuang W ; Peng J ; Chen L ; Zeng J Nat Commun 2017[Sep]; 8 (1 ): 573 PMID28924171 show ga The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations. |*Algorithms [MESH] |Animals [MESH] |Celecoxib/chemistry/metabolism [MESH] |Cells, Cultured [MESH] |Computational Biology/*methods [MESH] |Cyclooxygenase 2/chemistry/metabolism [MESH] |Drug Repositioning/*methods [MESH] |Humans [MESH] |Mice, Inbred C57BL [MESH] |Models, Molecular [MESH] |Pharmaceutical Preparations/chemistry/*metabolism [MESH] |Protein Binding [MESH] |Protein Domains [MESH]A network integration approach for drug-target interaction prediction (epmc).pdf DeepDyve Pubget Overpricing