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PEERJCS
2016

Computational drug repositioning based on side-effects mined from social media

11 months 25 days ago
Computational drug repositioning based on side-effects mined from social media
Drug repositioning methods attempt to identify novel therapeutic indications for marketed drugs. Strategies include the use of side-effects to assign new disease indications, based on the premise that both therapeutic effects and side-effects are measurable physiological changes resulting from drug intervention. Drugs with similar side-effects might share a common mechanism of action linking side-effects with disease treatment, or may serve as a treatment by “rescuing” a disease phenotype on the basis of their side-effects; therefore it may be possible to infer new indications based on the similarity of side-effect profiles. While existing methods leverage side-effect data from clinical studies and drug labels, evidence suggests this information is often incomplete due to under-reporting. Here, we describe a novel computational method that uses side-effect data mined from social media to generate a sparse undirected graphical model using inverse covariance estimation with ℓ1-nor...
Timothy Nugent, Vassilis Plachouras, Jochen L. Lei
Added 08 Apr 2016
Updated 08 Apr 2016
Type Journal
Year 2016
Where PEERJCS
Authors Timothy Nugent, Vassilis Plachouras, Jochen L. Leidner
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