ArticleScientific reports2021
Predicting the side effects of drugs using matrix factorization on spontaneous reporting database.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Causal knowledge graph analysis identifies adverse drug effects.Bioinformatics (Oxford, England) · 2026Article
- Development of a genetic priority score to predict drug side effects using human genetic evidence.Nature communications · 2025Article
- A novel weighted pseudo-labeling framework based on matrix factorization for adverse drug reaction prediction.BMC bioinformatics · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
The severe side effects of some drugs can threaten the lives of patients and financially jeopardize pharmaceutical companies. Computational methods utilizing chemical, biological, and phenotypic features have been used to address this problem by predicting the side effects. Among these methods, the matrix factorization method, which utilizes the side-effect history of different drugs, has yielded promising results. However, approaches that encapsulate all the characteristics of side-effect prediction have not been investigated to date. To address this gap, we applied the logistic matrix factorization algorithm to a database of spontaneous reports to construct a prediction with higher accuracy. We expressed the distinction in the importance of drug-side effect pairs by a weighting strategy and addressed the cold-start problem via an attribute-to-feature mapping method. Consequently, our proposed model improved the prediction accuracy by 2.5% and efficiently handled the cold-start problem. The proposed methodology is expected to benefit applications such as warning systems in clinical settings.
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