ReviewCurrent opinion in structural biology2024
A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data.
Review in Current opinion in structural biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- AI-driven multi-omics integration in breast cancer: clinical applications, immunotherapy prediction, and translational challenges.Frontiers in molecular biosciences · 2026Review
- DAGFormer: A graph-based domain adaptation approach for single-cell cancer drug response prediction.PLoS computational biology · 2025Article
- Single-cell RNA sequencing in osteosarcoma: applications in diagnosis, prognosis, and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Comprehensive analysis of mitochondria-associated genes in glioblastoma via single-cell and bulk RNA sequencing.Discover oncology · 2025Article
- Article
- Article
- Drug resistance and tumor heterogeneity: cells and ensembles.Biophysical reviews · 2025Review
- Identification of drug-resistant individual cells within tumors by semi-supervised transfer learning from bulk to single-cell transcriptome.Communications biology · 2025Article
- Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025Review
- A drug response prediction method for single-cell tumors combining attention networks and transfer learning.Frontiers in medicine · 2025Article
- Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.Nature communications · 2024Article
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.
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.