| Autor: |
Timothy Nugent, Vassilis Plachouras, Jochen L. Leidner |
| Jazyk: |
angličtina |
| Rok vydání: |
2016 |
| Předmět: |
|
| Zdroj: |
PeerJ Computer Science, Vol 2, p e46 (2016) |
| Druh dokumentu: |
article |
| ISSN: |
2376-5992 |
| DOI: |
10.7717/peerj-cs.46 |
| Popis: |
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-norm regularization. Results show that known indications are well recovered while current trial indications can also be identified, suggesting that sparse graphical models generated using side-effect data mined from social media may be useful for computational drug repositioning. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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