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10.1109/TCBB.2021.3085972 http://scihub22266oqcxt.onion/10.1109/TCBB.2021.3085972 34081583!8769035!34081583     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE/ACM+Trans+Comput+Biol+Bioinform 2021 ; 18 (4): 1290-1298 Nephropedia Template TP {{tp|p=34081583|t=2021. LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network |pdf=|usr=}}{{34081583}} gab.com Text LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network JO: IEEE/ACM Trans Comput Biol Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34081583 #FOAvip An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model. Twit Text FOAVip LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network ????IEEE/ACM Trans Comput Biol Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34081583 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34081583 #FOAvip English Wikipedia <ref>{{Cite pmid|34081583}}</ref> LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network #MMPMID34081583 Zhou D; Peng S; Wei DQ; Zhong W; Dou Y; Xie X IEEE/ACM Trans Comput Biol Bioinform 2021[Jul]; 18 (4): 1290-1298 PMID34081583show ga An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model. |*COVID-19 Drug Treatment[MESH] |*Neural Networks, Computer[MESH] |Antiviral Agents/*pharmacology[MESH] |COVID-19/epidemiology/*virology[MESH] |Computational Biology[MESH] |Databases, Pharmaceutical/statistics & numerical data[MESH] |Drug Development/methods/statistics & numerical data[MESH] |Drug Evaluation, Preclinical/*methods/statistics & numerical data[MESH] |Drug Repositioning/methods/statistics & numerical data[MESH] |Host Microbial Interactions/drug effects[MESH] |Humans[MESH] |Nonlinear Dynamics[MESH] |Pandemics[MESH]LUNAR :Drug Screening for Novel Coronavirus Based on Representation Le(epmc).pdf DeepDyve Pubget Overpricing