ArticleBMC bioinformatics2023
GPDRP: a multimodal framework for drug response prediction with graph transformer.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- MDCDR: predicting cancer drug response via multimodal feature fusion and feature disentanglement.Briefings in bioinformatics · 2026Article
- MoGraphDRP: Multi-omics and graph fusion with bilinear attention for predicting drug sensitivity.PloS one · 2026Article
- Advancing toxicity AI-based prediction with multilevel systems biology: a case study on genotoxicity.Briefings in bioinformatics · 2025Article
- Multiomics-Driven Drug-Cell Interaction Network for Chemotherapy Sensitivity Prediction in Metabolically Defined Triple-Negative Breast Cancer Subtypes.Journal of cellular and molecular medicine · 2025Article
- MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction.BMC bioinformatics · 2025Article
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
- Comparative evaluation of feature reduction methods for drug response prediction.Scientific reports · 2024Article
- A Health Monitoring Model for Circulation Water Pumps in a Nuclear Power Plant Based on Graph Neural Network Observer.Sensors (Basel, Switzerland) · 2024Article
- DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network.Frontiers in genetics · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundIn the field of computational personalized medicine, drug response prediction (DRP) is a critical issue. However, existing studies often characterize drugs as strings, a representation that does not align with the natural description of molecules. Additionally, they ignore gene pathway-specific combinatorial implication.
resultsIn this study, we propose drug Graph and gene Pathway based Drug response prediction method (GPDRP), a new multimodal deep learning model for predicting drug responses based on drug molecular graphs and gene pathway activity. In GPDRP, drugs are represented by molecular graphs, while cell lines are described by gene pathway activity scores. The model separately learns these two types of data using Graph Neural Networks (GNN) with Graph Transformers and deep neural networks. Predictions are subsequently made through fully connected layers.
conclusionsOur results indicate that Graph Transformer-based model delivers superior performance. We apply GPDRP on hundreds of cancer cell lines' bulk RNA-sequencing data, and it outperforms some recently published models. Furthermore, the generalizability and applicability of GPDRP are demonstrated through its predictions on unknown drug-cell line pairs and xenografts. This underscores the interpretability achieved by incorporating gene pathways.
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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.