ArticleBioinformatics (Oxford, England)2018
Predicting serious rare adverse reactions of novel chemicals.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
8 citing papers in PubMed.
- An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).Scientific reports · 2025Article
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study.Frontiers in pharmacology · 2025Article
- Hyperbolic matrix factorization improves prediction of drug-target associations.Scientific reports · 2023Article
- Machine learning prediction of side effects for drugs in clinical trials.Cell reports methods · 2022Article
- Predicting the side effects of drugs using matrix factorization on spontaneous reporting database.Scientific reports · 2021Article
- Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases.Nature reviews. Rheumatology · 2020Review
- Mining FDA resources to compute population-specific frequencies of adverse drug reactions.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.