ArticleBriefings in bioinformatics2024
PANCDR: precise medicine prediction using an adversarial network for cancer drug response.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- TMEDRP: decoding tumor-intrinsic and microenvironmental signatures for clinical drug response prediction.Bioinformatics (Oxford, England) · 2026Article
- Multiscale higher-order molecular simplicial complex embedding for drug response prediction.Bioinformatics (Oxford, England) · 2026Article
- Topology-Aware Deep Learning on Higher-Order Structures for Drug Response Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DR.DEGMON: self-explainable deep neural network for drug-induced cell viability prediction incorporating differentially expressed genes and gene ontology.BMC medical genomics · 2026Article
- Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors.NPJ precision oncology · 2026Article
- MDCDR: predicting cancer drug response via multimodal feature fusion and feature disentanglement.Briefings in bioinformatics · 2026Article
- The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.iScience · 2026Article
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- Biomedical applications of organoids derived from the digestive system.Frontiers in cell and developmental biology · 2025Review
- Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency.BMC bioinformatics · 2024Article
- Artificial intelligence, medications, pharmacogenomics, and ethics.Pharmacogenomics · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.
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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.