id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-313268-j51zyodw	Zeng, Xiangxiang	Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning	2020-07-12		.txt	text/plain	4081	217	42	Using Amazon's AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. 10−12 Deep learning has also recently demonstrated its better performance than classic machine learning methods to assist drug repurposing, 13 −16 yet without foreknowledge of the complex networks connecting drugs, targets, SARS-CoV-2, and diseases, the development of affordable approaches for the effective treatment of COVID-19 is challenging. Via systematic validation using transcriptomics and proteomics data generated from SARS-CoV-2-infected human cells and the ongoing clinical trial data, we successfully identified 41 drug candidates that can be further tested in large-scale randomized control trials for the potential treatment of COVID-19. Using Amazon's AWS computing resources, we identified 41 high-confidence repurposed drug candidates (including dexamethasone, indomethacin, niclosamide, and toremifene) for COVID-19, which were validated by an enrichment analysis of gene expression and proteomics data in SARS-CoV-2 infected human cells.	./cache/cord-313268-j51zyodw.txt	./txt/cord-313268-j51zyodw.txt