ReviewTrends in pharmacological sciences2020
Single-Cell Techniques and Deep Learning in Predicting Drug Response.
Review in Trends in pharmacological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- DiM2-DRP: Dual-Input Multi-View Dynamic Contrastive Learning Framework for Drug Response Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Targeting metabolism to combat anticancer and antibacterial drug resistance.Trends in pharmacological sciences · 2026Review
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- 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
- Inferring causal trajectories from spatial transcriptomics using CASCAT.Nucleic acids research · 2025Article
- Drug resistance and tumor heterogeneity: cells and ensembles.Biophysical reviews · 2025Review
- A Transfer Learning Framework for Predicting and Interpreting Drug Responses via Single-Cell RNA-Seq Data.International journal of molecular sciences · 2025Article
- Applications of Artificial Intelligence in Drug Repurposing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Liquid biopsies in cancer.Molecular biomedicine · 2025Review
- Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data.Expert opinion on drug discovery · 2024Review
- Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors.Cancer research · 2024Article
- A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data.Current opinion in structural biology · 2024Review
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- MMDRP: drug response prediction and biomarker discovery using multi-modal deep learning.Bioinformatics advances · 2024Article
- Dermoscopy-based Radiomics Help Distinguish Basal Cell Carcinoma and Actinic Keratosis: A Large-scale Real-world Study Based on a 207-combination Machine Learning Computational Framework.Journal of Cancer · 2024Article
- Unlocking ovarian cancer heterogeneity: advancing immunotherapy through single-cell transcriptomics.Frontiers in oncology · 2024Review
- Innovative aspects and applications of single cell technology for different diseases.American journal of cancer research · 2024Review
- Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles.bioRxiv : the preprint server for biology · 2023Article
- Recent advancements in single-cell metabolic analysis for pharmacological research.Journal of pharmaceutical analysis · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughlyinvestigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequencedata, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models.
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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.