id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_pmgmlohszzcrxlzdesctv3cf3y	Timothy Nugent	Computational drug repositioning based on side-effects mined from social media	2016	24	.pdf	application/pdf	11779	1622	20	Drug repositioning methods attempt to identify novel therapeutic indications for methods leverage side-effect data from clinical studies and drug labels, evidence describe a novel computational method that uses side-effect data mined from social sparse graphical models generated using side-effect data mined from social media Drug repositioning is the process of identifying novel therapeutic indications for marketed to perform large-scale mining of reported drug side-effects in near real-time from the In this study, we describe a drug repositioning methodology that uses side-effect data causality indicators, clinical trials, medical professional roles, side effect-triggers and drugs. x∈{0,1}, indicating whether each drug was reported to cause each side-effect in the Twitter Data-driven prediction of drug effects and drug side-effects and therapeutic indications. Systematic drug repositioning based on clinical side-effects. Computational drug repositioning based on side-effects mined from social media Computational drug repositioning based on side-effects mined from social media Computational drug repositioning based on side-effects mined from social media	./cache/work_pmgmlohszzcrxlzdesctv3cf3y.pdf	./txt/work_pmgmlohszzcrxlzdesctv3cf3y.txt