ArticleScientific reports2018
Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers, 1 of them a synthesis that pooled it.
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Who cites it
122 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- FastMI-HGNet: A Two-Stream Heterogeneous Graph Neural Network for Multi-Omics Disease Classification.Genes · 2026Article
- Gene dependency-informed inference of response to targeted cancer therapies.Nature communications · 2026Article
- NetPolicy-RL: network-informed offline reinforcement learning for pharmacogenomic drug prioritization.Journal of computer-aided molecular design · 2026Article
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- A scalable multimodal graph neural network for drug combination response prediction.Molecular diversity · 2026Article
- MMPCS: multi-view molecular pretraining based on consistency information and specific information.Bioinformatics (Oxford, England) · 2026Article
- MoGraphDRP: Multi-omics and graph fusion with bilinear attention for predicting drug sensitivity.PloS one · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Exploiting artificial intelligence in precision oncology: an updated comprehensive review.Journal of translational medicine · 2025Review
- DVFGCDR: a dual-view fusion graph neural network for cancer drug response prediction.NAR cancer · 2025Article
- Shaping Orthodontics of the Future: Concepts and Implications from a Cellular and Molecular Perspective.International journal of molecular sciences · 2025Review
- DeepCCDS: Interpretable Deep Learning Framework for Predicting Cancer Cell Drug Sensitivity through Characterizing Cancer Driver Signals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines.Journal of medical systems · 2025Article
- Increasing use of artificial intelligence in genomic medicine for cancer care- the promise and potential pitfalls.BJC reports · 2025Review
- A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes.iScience · 2025Article
- SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction.bioRxiv : the preprint server for biology · 2025Article
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
62 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based on a large-scale drug screening assay data encompassing genomic profiles of 787 human cancer cell lines and structural profiles of 244 drugs. CDRscan employs a two-step convolution architecture, where the genomic mutational fingerprints of cell lines and the molecular fingerprints of drugs are processed individually, then merged by 'virtual docking', an in silico modelling of drug treatment. Analysis of the goodness-of-fit between observed and predicted drug response revealed a high prediction accuracy of CDRscan (R
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