ArticleBMC bioinformatics2022
DualGCN: a dual graph convolutional network model to predict cancer drug response.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- A Holistic Review of Oncological Drug Targets and Trajectories of Resistance in Cancer Therapy.Oncology research · 2026Review
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 2025Article
- Review
- Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.Interdisciplinary sciences, computational life sciences · 2025Article
- AdapTor: Adaptive Topological Regression for quantitative structure-activity relationship modeling.Journal of cheminformatics · 2025Article
- Deep learning enhanced deciphering of brain activity maps for discovery of therapeutics for brain disorders.iScience · 2025Article
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- scTML: a pan-cancer single-cell landscape of multiple mutation types.Nucleic acids research · 2025Article
- Drug discovery and mechanism prediction with explainable graph neural networks.Scientific reports · 2025Article
- Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025Review
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- A precision oncology-focused deep learning framework for personalized selection of cancer therapy.bioRxiv : the preprint server for biology · 2024Article
- Cancer drug sensitivity estimation using modular deep Graph Neural Networks.NAR genomics and bioinformatics · 2024Article
- DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies.BMC bioinformatics · 2024Article
- NeoHunter: Flexible software for systematically detecting neoantigens from sequencing data.Quantitative biology (Beijing, China) · 2024Article
- Multi-omics based artificial intelligence for cancer research.Advances in cancer research · 2024Review
- DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction.Quantitative biology (Beijing, China) · 2023Article
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6 authors.
Funding
Abstract
backgroundDrug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future.
resultsWe present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein-protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data.
conclusionsThe proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.
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