ArticleBMC bioinformatics2018
TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- MFCN-DDI: Capsule network based on multimodal feature for multitype drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.Scientific reports · 2025Article
- Improving drug-drug interaction prediction via in-context learning and judging with large language models.Frontiers in pharmacology · 2025Article
- Predicting the toxic side effects of drug interactions using chemical structures and protein sequences.Scientific reports · 2024Article
- Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.Journal of translational medicine · 2024Article
- Drug-drug interaction prediction: databases, web servers and computational models.Briefings in bioinformatics · 2023Review
- Article
- Predict multi-type drug-drug interactions in cold start scenario.BMC bioinformatics · 2022Article
- PINet 1.0: A pathway network-based evaluation of drug combinations for the management of specific diseases.Frontiers in molecular biosciences · 2022Article
- A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning.Frontiers in pharmacology · 2021Review
- Recommender Systems in Antiviral Drug Discovery.ACS omega · 2020Article
- A Machine Learning Method for Drug Combination Prediction.Frontiers in genetics · 2020Article
- Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings.BMC bioinformatics · 2019Article
- Detecting drug communities and predicting comprehensive drug-drug interactions via balance regularized semi-nonnegative matrix factorization.Journal of cheminformatics · 2019Article
- Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures.PloS one · 2019Article
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7 authors.
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Abstract
backgroundA significant number of adverse drug reactions is caused by unexpected Drug-drug interactions (DDIs). The identification of DDIs becomes crucial before the co-prescription of multiple drugs is made. Such a task in clinics or in drug discovery usually requires high costs and numerous limitations, while computational approaches are able to predict potential DDIs effectively by utilizing diverse drug attributes (e.g. side effects). Nevertheless, they're incapable when required to predict enhancive and degressive DDIs, which change increasingly and decreasingly the pharmacological behavior of interacting drugs respectively. The pharmacological change of DDIs is one of the most important factors when making a multi-drug prescription.
resultsIn this work, we design a Triple Matrix Factorization-based Unified Framework (TMFUF) to address the above issue. By leveraging a group of side effect entries of drugs, TMFUF achieves the inspiring result (AUC = 0.842 and AUPR = 0.526) in the case of conventional DDI prediction under the traditional screening task. In the comparison with two state-of-the-art approaches, TMFUF demonstrates it superiority by ~ 7% and ~ 20% improvement in terms of AUC and AUPR respectively. More importantly, TMFUF shows its ability in the comprehensive DDI prediction under different screening tasks. Finally, a utilization TMFUF reveals the significant pairs of side effects, which contribute to form enhancive and degressive DDIs, for further clinical validation.
conclusionsThe proposed TMFUF is first capable to predict both conventional binary DDIs and comprehensive DDIs such that it captures the pharmacological changes caused by DDIs. Furthermore, it provides a unified solution of DDI prediction for two screening scenarios, which involves newly given drugs having no prior interaction. Another advantage is its ability to indicate how significantly the pairs of drug features contribute to form DDIs.
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