ArticleBriefings in bioinformatics2022
An overview of machine learning methods for monotherapy drug response prediction.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Topology-Aware Deep Learning on Higher-Order Structures for Drug Response Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Critical evaluation of drug response prediction models with DrEval.Nature communications · 2026Article
- Molecular Insights into Phage-Hydrogel Polymer Interactions Through Docking, Molecular Dynamics, and Machine Learning.Polymers · 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
- Machine Learning-Based Analysis of Large-Scale Transcriptomic Data Identifies Core Genes Associated with Multi-Drug Resistance.International journal of molecular sciences · 2026Article
- Machine Learning Prediction of Pharmacogenetic Testing Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Article
- A RAIR-ATC transcriptional axis and multimodal drug-response modelling reveal class-level vulnerabilities in thyroid cancer.Frontiers in pharmacology · 2026Article
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- CellHit: a web server to predict and analyze cancer patients' drug responsiveness.Nucleic acids research · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes.iScience · 2025Article
- The specification game: rethinking the evaluation of drug response prediction for precision oncology.Journal of cheminformatics · 2025Article
- sTPLS: identifying common and specific correlated patterns under multiple biological conditions.Briefings in bioinformatics · 2025Article
- Learning and actioning general principles of cancer cell drug sensitivity.Nature communications · 2025Article
- The Hallmarks of Predictive Oncology.Cancer discovery · 2025Article
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.
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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.