id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-263008-w6twrjzr	Yin, Rui	Alignment-free machine learning approaches for the lethality prediction of potential novel human-adapted coronavirus using genomic nucleotide	2020-07-15		.txt	text/plain	3684	213	48	title: Alignment-free machine learning approaches for the lethality prediction of potential novel human-adapted coronavirus using genomic nucleotide We developed alignment-free machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of potential human-adapted coronavirus using genomic nucleotide. We performed extensive experiments through six different feature transformation and machine learning algorithms in combination with digital signal processing to infer the lethality of possible future novel coronaviruses using previous existing strains. The results demonstrate that, for any novel human coronavirus strains, this alignment-free machine learning-based approach can offer a reliable real-time estimation for its viral lethality. In this paper, we propose alignment-free machine learning-based approaches to infer 81 2/18 the lethality of potential novel human-adapted coronavirus using genomic sequences. We provide a comprehensive analysis through alignment-free machine learning-based 385 methods for the prediction of the lethality of potential human-adapted coronavirus.	./cache/cord-263008-w6twrjzr.txt	./txt/cord-263008-w6twrjzr.txt