id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-328069-a9fi9ssg	Pathan, Refat Khan	Time Series Prediction of COVID-19 by Mutation Rate Analysis using Recurrent Neural Network-based LSTM Model	2020-06-13		.txt	text/plain	3402	202	64	title: Time Series Prediction of COVID-19 by Mutation Rate Analysis using Recurrent Neural Network-based LSTM Model This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. 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. Using this train and testing process, the nucleotide mutation rate of 400(th) patient in future time has been predicted. The complete genomic sequence (Wuhan-HU1) of this large RNA virus (SARS-CoV-2) was first discovered in the laboratory of China on 10th January [10] and placed in the NCBI GenBank. al have performed Phylogenetic analysis of SARS-CoV-2 virus based on the spike gene of the genomic sequence [17] . An adequate amount of gene dataset is currently available in the NCBI GenBank which has the complete genome sequence of SARS-CoV-2.	./cache/cord-328069-a9fi9ssg.txt	./txt/cord-328069-a9fi9ssg.txt