ReviewCell reports methods2023
Machine learning approaches to predict drug efficacy and toxicity in oncology.
Review in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed.
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Methods for Detecting Suspicious Information From Individual Transactions of Pharmaceutical Products via Twitter (now X): Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Nanotechnology in Cancer Therapy: How Nanoparticles Are Shaping the Future of Personalized Treatment.ACS nano medicine · 2026Review
- AI for clinical trials in oncology.ESMO real world data and digital oncology · 2026Review
- Benchmarking deep learning models for predicting anticancer drug potency (ICCommunications chemistry · 2026Article
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Replication Stress in Cancer: Mechanistic Insights and Therapeutic Opportunities for Radiosensitization.Current issues in molecular biology · 2026Review
- Exploring Artificial Intelligence Integration in Indian Pharmacology: A Survey on Scope, Threats, and Challenges.Annals of African medicine · 2026Article
- Integrating Microarray Analysis, Machine Learning, and Molecular Docking to Explore the Mechanism of Doxorubicin-induced Cardiotoxicity.Current medicinal chemistry · 2026Article
- Investigating the Mechanisms of Pazopanib-induced Hepatotoxicity: Insights from Network Toxicology, Microarray Analysis, and Machine Learning.Current medicinal chemistry · 2026Article
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Article
- A human metabolic map of pharmacological perturbations reveals drug modes of action.Nature biotechnology · 2025Article
- Drug toxicity prediction based on genotype-phenotype differences between preclinical models and humans.EBioMedicine · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Preclinical ex vivo and in vivo models to study immunotherapy agents and their combinations as predictive tools toward the clinic.Journal for immunotherapy of cancer · 2025Review
- A Collaborative Data Sharing Platform to Accelerate Translation of Biomedical Innovations.Bioengineering (Basel, Switzerland) · 2025Article
- Optimizing tacrolimus dosage in post-renal transplantation using DoseOptimal framework: profiling CYP3A5 genetic variants for interpretability.International journal of clinical pharmacy · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical production. MLAs have the potential to provide valuable insights and predictions in these areas by representing both the disease state and the therapeutic agents used to treat it. To fully utilize the capabilities of MLAs in oncology, it is important to understand the fundamental concepts underlying these algorithms and how they can be applied to assess the efficacy and toxicity of therapeutics. In this perspective, we lay out approaches to represent both the disease state and the therapeutic agents used by MLAs to derive novel insights and make relevant predictions.
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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.