Predicting serious rare adverse reactions of novel chemicals.
Ontology highlight
ABSTRACT: 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 https://github.com/poleksic/side-effects.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Poleksic A
PROVIDER: S-EPMC6084596 | biostudies-literature | 2018 Aug
REPOSITORIES: biostudies-literature
Publications
Predicting serious rare adverse reactions of novel chemicals.
Poleksic Aleksandar A Xie Lei L
Bioinformatics (Oxford, England) 20180801 16
<h4>Motivation</h4>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.<h4>Results</h4>We present a novel method, based on the theory of 'compressed sensing' (CS), which can accurately pre ...[more]
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