ReviewResearch (Washington, D.C.)2023
Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.
Review in Research (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Cell type-specific contextualisation of the human phenome: towards the systematic treatment of all rare diseases.Genome medicine · 2026Article
- Identifying potential ligand-receptor interactions by integrating LSTM network and the attention mechanism for cell-cell communication prediction.Journal of translational medicine · 2026Article
- ProtFormer-Site: Ultra-fast and Accurate Prediction of Protein-Protein Interaction Sites with Protein Language Model.Interdisciplinary sciences, computational life sciences · 2026Article
- Mri-based habitat and peritumoral radiomics for predicting the proliferative activity of stromal cells in giant cell tumor of bone.Journal of bone oncology · 2026Article
- Integrative Mendelian Randomization and Single-Cell Pseudotime Analysis Reveal DKK3 as a PI3K-AKT-Modulated Driver of Esophageal Squamous Cell Carcinoma.Human mutation · 2026Article
- Mapping Current Research Status and Emerging Frontiers of T-Cell Exhaustion: A Comprehensive Data-Mining-Based Study.Annals of surgical oncology · 2026Article
- Biological sequence analysis: Advances, medical applications, and challenges.Fundamental research · 2026Review
- Dissecting tumor heterogeneity in colorectal cancer: uncovering the role of BCL2L1Frontiers in immunology · 2026Article
- RWRGDR: Random Walk and GraphSAGE-based Framework for Enhanced Drug Repositioning.Current drug targets · 2026Article
- Machine Learning Prediction of Poor Flexion-Extension Outcome After Open Elbow Arthrolysis: Identifying Individual Predisposition Using Clinical and Laboratory Indicators.Orthopaedic journal of sports medicine · 2025Article
- Deferoxamine addresses metabolic dysregulation and urinary tract infections in weight-associated gestational diabetes mellitus.European journal of medical research · 2025Article
- Progress and applications of single-cell RNA sequencing and spatial transcriptome technology in acute kidney injury research.Molecular therapy. Nucleic acids · 2025Review
- Accurate prediction of toxicity peptide and its function using multi-view tensor learning and latent semantic learning framework.Bioinformatics (Oxford, England) · 2025Article
- Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer.BMC bioinformatics · 2025Article
- Single-cell sequencing reveals PHLDA1-positive smooth muscle cells promote local invasion in head and neck squamous cell carcinoma.Translational oncology · 2025Article
- Multi-view clustering for single-cell RNA-seq data based on graph fusion.Briefings in bioinformatics · 2025Article
- Multiscale graph equivariant diffusion model for 3D molecule design.Science advances · 2025Article
- HNF-DDA: subgraph contrastive-driven transformer-style heterogeneous network embedding for drug-disease association prediction.BMC biology · 2025Article
- Single-cell insights into HNSCC tumor heterogeneity and programmed cell death pathways.Translational oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.
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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.