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Motivation: Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the world's economy. Due to the limitations of the animal model, computational prediction of serious and rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing.

Results: We present a novel method, based on the theory of 'compressed sensing' (CS), which can accurately predict serious side-effects of candidate and market drugs. Not only is our method able to infer new chemical-ADR associations using existing noisy, biased and incomplete databases, but our data also demonstrate that the accuracy of CS in predicting a serious ADR for a candidate drug increases with increasing knowledge of other ADRs associated with the drug. In practice, this means that as the candidate drug moves up the different stages of clinical trials, the prediction accuracy of our method will increase accordingly.

Availability and implementation: The program is available at

Supplementary information: Supplementary data are available at Bioinformatics online.


This article was originally published in Bioinformatics, available at DOI: 10.1093/bioinformatics/bty193.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (


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