ReviewNPJ precision oncology2020
Machine learning approaches to drug response prediction: challenges and recent progress.
Review in NPJ precision oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers, 3 of them syntheses 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
183 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Harnessing meta-analysis and artificial intelligence to reveal conserved regulatory biosignatures of abiotic stress in soybean.Biology direct · 2026Pooled it
- Machine learning for predicting treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in rheumatoid arthritis: a scoping review.BMC rheumatology · 2025Pooled it
- Pooled it
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- DiM2-DRP: Dual-Input Multi-View Dynamic Contrastive Learning Framework for Drug Response Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Machine learning and multi-omics clustering to map cellular rewiring and immune evasion in ccRCC.Human genomics · 2026Article
- What Is Precision Psychotherapy?The Journal of medicine and philosophy · 2026Article
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- ProteinNetworkSight: a user-friendly platform for transforming co-expression patterns into actionable therapeutic insights through interactive network visualization.Nucleic acids research · 2026Article
- DiSPA: differential substructure-pathway attention for drug response prediction.Bioinformatics (Oxford, England) · 2026Article
- Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers.bioRxiv : the preprint server for biology · 2026Article
- Gene dependency-informed inference of response to targeted cancer therapies.Nature communications · 2026Article
- Review
- A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer.Cell reports methods · 2026Article
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.BMC bioinformatics · 2026Article
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- DeepDrugs: a mechanism-aware tri-linear attention framework for synergistic drug-combination prediction.Briefings in bioinformatics · 2026Article
- Machine Learning Prediction of Pharmacogenetic Testing Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis.Briefings in bioinformatics · 2026Article
123 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient's chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.
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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.