ArticleNature communications2022
Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.
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Who cites it
116 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integration of single-cell and bulk RNA-sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer.Molecular oncology · 2025Pooled it
- Leveraging neural network models for drug repurposing: a case study on cardiac hypertrophy.Scientific reports · 2026Article
- A disentangled transformer-based transfer learning framework to predict patient drug response from tumor single-cell transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- scRADAR: Dissecting intratumoral drug response heterogeneity at single-cell resolution via mechanism-guided prototype routing.PLoS computational biology · 2026Article
- FourierDrug: a domain generalization framework for robust drug response prediction via frequency-space asymmetric attention.Bioinformatics (Oxford, England) · 2026Article
- Scoring gene importance by interpreting single-cell foundation models.Nature biotechnology · 2026Article
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Decoding drug-responsive cell subpopulations in triple-negative breast cancer using single-cell multiomics.iScience · 2026Article
- scTumorDrug: predicting cell-type-specific drug responses for heterogeneous tumors.Briefings in bioinformatics · 2026Article
- A multicentre analysis of efficacy, safety and molecular response correlates of fostamatinib in warm autoimmune haemolytic anaemia and Evans syndrome.British journal of haematology · 2026Article
- scMILD: Single-cell multiple instance learning for sample classification and associated subpopulation discovery.iScience · 2026Article
- Context defines precision: rethinking KRAS inhibition in oncology.NPJ precision oncology · 2026Review
- scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- scGPT: end-to-end protocol for fine-tuned retinal cell type annotation.Nature protocols · 2026Review
- TiRank prioritizes phenotypic niches in tumor microenvironment for clinical biomarker discovery.Genome medicine · 2026Article
- Medea: An omics AI agent for therapeutic discovery.bioRxiv : the preprint server for biology · 2026Article
- scDrugAtlas: an integrative single-cell drug response database for dissecting tumour heterogeneity in therapeutic efficacy.Database : the journal of biological databases and curation · 2026Article
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.Communications biology · 2026Article
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
56 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model trained on bulk RNA-seq data to predict drug responses in scRNA-seq. Another feature of scDEAL is the integrated gradient feature interpretation to infer the signature genes of drug resistance mechanisms. We benchmark scDEAL on six scRNA-seq datasets and demonstrate its model interpretability via three case studies focusing on drug response label prediction, gene signature identification, and pseudotime analysis. We believe that scDEAL could help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy.
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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.