ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023
Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 49 citations in OpenAlex.
- A disentangled transformer-based transfer learning framework to predict patient drug response from tumor single-cell transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- 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
- scTumorDrug: predicting cell-type-specific drug responses for heterogeneous tumors.Briefings in bioinformatics · 2026Article
- Illuminating cell states by a comprehensive and interpretable single cell foundation model.Nature communications · 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
- Algorithmically defined therapeutic targets: integrating single-cell transfer learning frameworks with small molecule drugs to reverse disease-associated cell fates.Frontiers in pharmacology · 2026Review
- Omics-based large language models: A new engine for drug discovery innovation.Acta pharmaceutica Sinica. B · 2026Review
- Personalized chronotherapy in glioblastoma: integrating circadian profiling and PK-PD modelling to optimize temozolomide timing.NPJ precision oncology · 2025Article
- DAGFormer: A graph-based domain adaptation approach for single-cell cancer drug response prediction.PLoS computational biology · 2025Article
- The applications of single-cell multiomics in drug screening.Pharmaceutical science advances · 2025Review
- scMFF: a machine learning framework with multiple feature fusion strategies for cell type identification.BMC bioinformatics · 2025Article
- scGSDR: Harnessing gene semantics for single-cell pharmacological profiling.Communications biology · 2025Article
- Biology-driven insights into the power of single-cell foundation models.Genome biology · 2025Article
- Adaptive individualized gene pair signatures distinguishing melanoma and predicting response to immune checkpoint blockade.iScience · 2025Article
- scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.Briefings in bioinformatics · 2025Article
- Identification of drug-resistant individual cells within tumors by semi-supervised transfer learning from bulk to single-cell transcriptome.Communications biology · 2025Article
- Single-Cell RNA Sequencing in Unraveling Acquired Resistance to EGFR-TKIs in Non-Small Cell Lung Cancer: New Perspectives.International journal of molecular sciences · 2025Review
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
The single-cell RNA sequencing (scRNA-seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA-seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti-cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA-Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single-cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre-clinical cell lines to infer pre-existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug-resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat-resistant while Gefitinib-sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution.
Indexed as
Identifiers
What OpenQuestion holds
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.