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10.1016/j.biosystems.2020.104315 http://scihub22266oqcxt.onion/10.1016/j.biosystems.2020.104315 33358827!7834057!33358827     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\33358827.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biosystems 2021 ; 201 (ä): 104315 Nephropedia Template TP {{tp|p=33358827|t=2021. A protein folding robot driven by a self-taught agent |pdf=|usr=}}{{33358827}} gab.com Text A protein folding robot driven by a self-taught agent JO: Biosystems http://www.kidney.de/pget.php?&tw=1&pmid=33358827 #FOAvip This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to solve HEs folding episodes. The proposed robotic unfolded structure inhabits a dynamic environment and is driven by a self-taught neural agent. The neural agent can read sensors and control the angles and interactions between individual amino acids. During the training phase, the agent uses reinforcement learning to explore new folding forms that conduce toward more significant rewards. The memory of the agent is implemented with neural networks. These neural networks are noise-balanced trained to satisfy the look for future conditions required by the Bellman equation. In the operating phase, the components merge into a wise up protein folding robot with look-ahead capacities, which consistently solves a section of the HEs protein. Twit Text FOAVip A protein folding robot driven by a self-taught agent ????Biosystems http://www.kidney.de/pget.php?&tw=1&pmid=33358827 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33358827 #FOAvip English Wikipedia <ref>{{Cite pmid|33358827}}</ref> A protein folding robot driven by a self-taught agent #MMPMID33358827 Chang O; Gonzales-Zubiate FA; Zhinin-Vera L; Valencia-Ramos R; Pineda I; Diaz-Barrios A Biosystems 2021[Mar]; 201 (ä): 104315 PMID33358827show ga This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to solve HEs folding episodes. The proposed robotic unfolded structure inhabits a dynamic environment and is driven by a self-taught neural agent. The neural agent can read sensors and control the angles and interactions between individual amino acids. During the training phase, the agent uses reinforcement learning to explore new folding forms that conduce toward more significant rewards. The memory of the agent is implemented with neural networks. These neural networks are noise-balanced trained to satisfy the look for future conditions required by the Bellman equation. In the operating phase, the components merge into a wise up protein folding robot with look-ahead capacities, which consistently solves a section of the HEs protein. |*Protein Folding[MESH] |Algorithms[MESH] |Amino Acid Sequence[MESH] |Computer Simulation[MESH] |Coronavirus/chemistry[MESH] |Hemagglutinins, Viral/chemistry[MESH] |Humans[MESH] |Machine Learning[MESH] |Models, Molecular[MESH] |Neural Networks, Computer[MESH] |Protein Conformation[MESH] |Robotics/*methods/statistics & numerical data[MESH] |Systems Analysis[MESH] |Systems Biology[MESH] |Viral Fusion Proteins/chemistry[MESH]A protein folding robot driven by a self-taught agent (epmc).pdf DeepDyve Pubget Overpricing