ArticleBMC systems biology2018
Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Knowledge-graph-enhanced multi-scale modeling for drug-drug interaction prediction.Molecular therapy. Nucleic acids · 2026Article
- MDG-DDI: multi-feature drug graph for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.Scientific reports · 2025Article
- Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset.Briefings in bioinformatics · 2025Article
- HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.BMC bioinformatics · 2025Article
- Improving drug-drug interaction prediction via in-context learning and judging with large language models.Frontiers in pharmacology · 2025Article
- Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.Journal of translational medicine · 2024Article
- Drug-drug interactions prediction based on deep learning and knowledge graph: A review.iScience · 2024Review
- Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.Scientific reports · 2024Article
- KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery.Briefings in bioinformatics · 2024Article
- Drug-drug interaction prediction: databases, web servers and computational models.Briefings in bioinformatics · 2023Review
- MSEDDI: Multi-Scale Embedding for Predicting Drug-Drug Interaction Events.International journal of molecular sciences · 2023Article
- A dual graph neural network for drug-drug interactions prediction based on molecular structure and interactions.PLoS computational biology · 2023Article
- Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network.Chemical science · 2022Article
- Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information.NPJ digital medicine · 2022Article
- Article
- Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs.Molecules (Basel, Switzerland) · 2022Article
- CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks.BMC bioinformatics · 2022Article
- Predict multi-type drug-drug interactions in cold start scenario.BMC bioinformatics · 2022Article
- MFDA: Multiview fusion based on dual-level attention for drug interaction prediction.Frontiers in pharmacology · 2022Article
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7 authors.
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Abstract
backgroundDrug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription.
resultsIn this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs.
conclusionsOur approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree and negative degree; (3) though the binary DDI network contains no information about enhancive and degressive DDIs at all, it implies some of their relationship in the comprehensive DDI matrix; (4) the occurrence of signs indicating enhancive and degressive DDIs is not random because the comprehensive DDI network is equipped with a structural balance.
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