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10.1016/j.chaos.2020.110018 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110018 C7293453!7293453 !32565626     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32565626 &cmd=llinks): Failed to open stream: HTTP request failed! 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Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model |pdf=|usr=}}{{32565626 }} gab.com Text Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32565626 #FOAvip SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400(th) patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time. Twit Text FOAVip Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32565626 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32565626 #FOAvip English Wikipedia <ref>{{Cite pmid|32565626 }}</ref> Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model #MMPMID32565626 Pathan RK ; Biswas M ; Khandaker MU Chaos Solitons Fractals 2020[Sep]; 138 (ä): 110018 PMID32565626 show ga SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400(th) patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time. äTime series prediction of COVID-19 by mutation rate analysis using rec(epmc).pdf DeepDyve Pubget Overpricing