ReviewSeminars in cancer biology2021
Machine and deep learning approaches for cancer drug repurposing.
Review in Seminars in cancer biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers, 3 of them syntheses that pooled it.
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
138 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- The landscape of the methodology in drug repurposing using human genomic data: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer.Frontiers in public health · 2022Trial
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- Uncoupling Type I Interferon Benefits From Inflammatory Toxicity: Transformer-Prioritized Precision Agonists for Potent and Safer Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- FOSL2 drives transcriptional activation of super‑enhancer-regulatedMolecular medicine reports · 2026Article
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- Applications and prospects of artificial intelligence and digital medicine in pediatric nasal skull base tumors.Pediatric investigation · 2026Review
- ZDHHC5: a pivotal palmitoyltransferase orchestrating signaling networks - unraveling mechanisms and therapeutic horizons.Biomarker research · 2026Review
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- Sex-specific machine learning models for carotid plaque prediction in individuals with fatty liver disease: a cross-sectional study.BMJ open · 2026Article
- A Neuroendocrine Differentiation-related Molecular Model for Prognosis Prediction in Prostate Cancer Patients.Current medicinal chemistry · 2026Article
- Quantum Computing in Drug Discovery Techniques, Challenges, and Emerging Opportunities.Current drug discovery technologies · 2026Review
- Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy.Acta pharmaceutica Sinica. B · 2026Article
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- From Tea to Topical Agent: Machine Learning and Bioinformatics Reveal KU DING Tea's Anti-UV Ingredients and Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2025Article
- DCAF13 Regulates Cell Proliferation and Immune Escape of Hepatocellular Carcinoma Through Activating the NF-κB Pathway.Cell biochemistry and biophysics · 2025Article
- Development and validation of a machine learning-based prognostic model for gastric cancer: a multicenter retrospective study.Langenbeck's archives of surgery · 2025Article
78 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
4 authors.
Funding
Abstract
Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.
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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.