ArticleInternational journal of molecular sciences2022
NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction.Journal of translational medicine · 2025Article
- MADSP: predicting anti-cancer drug synergy through multi-source integration and attention-based representation learning.Bioinformatics (Oxford, England) · 2025Article
- Predicting Synergistic Drug Combinations Based on Fusion of Cell and Drug Molecular Structures.Interdisciplinary sciences, computational life sciences · 2025Article
- HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction.Journal of chemical information and modeling · 2024Article
- A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer.Interdisciplinary sciences, computational life sciences · 2024Article
- Pyrazoline B-Paclitaxel or Doxorubicin Combination Drugs Show Synergistic Activity Against Cancer Cells: In silico Study.Advances and applications in bioinformatics and chemistry : AABC · 2024Article
- Combination therapy synergism prediction for virus treatment using machine learning models.PloS one · 2024Article
- DeepTraSynergy: drug combinations using multimodal deep learning with transformers.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug-cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug-cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug-cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases.
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Registered trials
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