ArticleBioinformatics (Oxford, England)2021
SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 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
48 citing papers in PubMed, 162 citations in OpenAlex.
- IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Tail-aware heterogeneous graph neural networks for multi-class drug-drug interaction prediction.Bioinformatics (Oxford, England) · 2026Article
- DisenKGE-DDI: A Knowledge Graph Embedding Framework Based on Disentangled Graph Attention Networks for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- NovelHTI: An Interpretable Pathway-Enhanced Framework for De Novo Target Prediction of Medicinal Herbs via Cross-Scale Heterogeneous Information Fusion.Pharmaceuticals (Basel, Switzerland) · 2026Article
- MFCN-DDI: Capsule network based on multimodal feature for multitype drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Addressing model overcomplexity in drug-drug interaction prediction with molecular fingerprints.Journal of cheminformatics · 2026Article
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- TRACE-DDI: A Hybrid Framework of Transformer-GAT Context Encoder and Pathway-Anchored Knowledge Graphs for DDI Prediction.Computational and structural biotechnology journal · 2026Article
- Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes.Bioinformatics (Oxford, England) · 2025Article
- LLM-Enhanced Multimodal Framework for Drug-Drug Interaction Prediction.Biomedicines · 2025Article
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction.Journal of pharmaceutical analysis · 2025Article
- Graph neural network-based drug-drug interaction prediction.Scientific reports · 2025Article
- DOME: Directional medical embedding vectors from Electronic Health Records.Journal of biomedical informatics · 2025Article
- A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.Briefings in functional genomics · 2025Review
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Explainable Artificial Intelligence in the Field of Drug Research.Drug design, development and therapy · 2025Review
- A graph attention-based deep learning network for predicting biotech-small-molecule drug interactions.Bioinformatics advances · 2025Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
motivationThanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task.
resultsTo fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. AVAILABILITY AND IMPLEMENTATION: The code is available in Supplementary Material. 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.