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10.3390/v12050560 http://scihub22266oqcxt.onion/10.3390/v12050560 32438586!7290575!32438586     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Viruses 2020 ; 12 (5): ä Nephropedia Template TP {{tp|p=32438586|t=2020. Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data |pdf=|usr=}}{{32438586}} gab.com Text Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data JO: Viruses http://www.kidney.de/pget.php?&tw=1&pmid=32438586 #FOAvip The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between "black box" deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data. Twit Text FOAVip Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data ????Viruses http://www.kidney.de/pget.php?&tw=1&pmid=32438586 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32438586 #FOAvip English Wikipedia <ref>{{Cite pmid|32438586}}</ref> Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data #MMPMID32438586 Steiner MC; Gibson KM; Crandall KA Viruses 2020[May]; 12 (5): ä PMID32438586show ga The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between "black box" deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data. |*Deep Learning[MESH] |Anti-Retroviral Agents/pharmacology[MESH] |Databases, Genetic[MESH] |Drug Resistance, Viral/*genetics[MESH] |Genotype[MESH] |HIV Infections/virology[MESH] |HIV-1/*drug effects/*genetics[MESH] |Humans[MESH] |Mutation[MESH] |Neural Networks, Computer[MESH]Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Seq(epmc).pdf DeepDyve Pubget Overpricing