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10.1038/s42256-020-00285-9 http://scihub22266oqcxt.onion/10.1038/s42256-020-00285-9 33796820!8009091!33796820     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 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 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Mach+Intell 2021 ; 3 (3): 247-257 Nephropedia Template TP {{tp|p=33796820|t=2021. A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing |pdf=|usr=}}{{33796820}} gab.com Text A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing JO: Nat Mach Intell http://www.kidney.de/pget.php?&tw=1&pmid=33796820 #FOAvip Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism. Chemical-induced gene expression profile provides a mechanistic signature of phenotypic response. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Few methods can perform phenotype-based de novo chemical compound screening. Here, we propose a mechanism-driven neural network-based method DeepCE, which utilizes graph neural network and multi-head attention mechanism to model chemical substructure-gene and gene-gene associations, for predicting the differential gene expression profile perturbed by de novo chemicals. Moreover, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves superior performances to state-of-the-art methods. The effectiveness of gene expression profiles generated from DeepCE is further supported by comparing them with observed data for downstream classification tasks. To demonstrate the value of DeepCE, we apply it to drug repurposing of COVID-19, and generate novel lead compounds consistent with clinical evidence. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data and screening novel chemicals for the modulation of a systemic response to disease. Twit Text FOAVip A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing ????Nat Mach Intell http://www.kidney.de/pget.php?&tw=1&pmid=33796820 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33796820 #FOAvip English Wikipedia <ref>{{Cite pmid|33796820}}</ref> A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing #MMPMID33796820 Pham TH; Qiu Y; Zeng J; Xie L; Zhang P Nat Mach Intell 2021[Mar]; 3 (3): 247-257 PMID33796820show ga Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism. Chemical-induced gene expression profile provides a mechanistic signature of phenotypic response. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Few methods can perform phenotype-based de novo chemical compound screening. Here, we propose a mechanism-driven neural network-based method DeepCE, which utilizes graph neural network and multi-head attention mechanism to model chemical substructure-gene and gene-gene associations, for predicting the differential gene expression profile perturbed by de novo chemicals. Moreover, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves superior performances to state-of-the-art methods. The effectiveness of gene expression profiles generated from DeepCE is further supported by comparing them with observed data for downstream classification tasks. To demonstrate the value of DeepCE, we apply it to drug repurposing of COVID-19, and generate novel lead compounds consistent with clinical evidence. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data and screening novel chemicals for the modulation of a systemic response to disease. äA deep learning framework for high-throughput mechanism-driven phenoty(epmc).pdf DeepDyve Pubget Overpricing