id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-232446-vvb2ffhv	Mongia, Aanchal	A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials	2020-07-03		.txt	text/plain	7123	382	47	In view to assist acceleration of this process (by pruning down the search space), we create and share a publicly available DVA database, along with a number of matrix completion techniques (mentioned above) for drug-virus association prediction. Such a computational approach requires the chemical structure of the drugs and, in case of graph-regularized matrix completion techniques, the genome of the viruses, or existing associations otherwise. A clear observation from the experiments is that the graph regularized-based matrix completion algorithms that incorporate the similarity information associated with the drugs and viruses, perform fairly well giving an AUC greater or equal than 0.83 in CV1. It can be noted that the standard matrix completion methods, which do not take into account the metadata, fail to learn from the association data giving a near-random performance as far as the prediction on novel viruses is concerned, depicting how very important the similarity information is.	./cache/cord-232446-vvb2ffhv.txt	./txt/cord-232446-vvb2ffhv.txt