ArticleScientific reports2020
Feature selection strategies for drug sensitivity prediction.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Towards convergence of AI and blockchain for personalized medicine in pharmacogenomics.Scientific reports · 2026Article
- GCNPath: introspecting drug response prediction with pathway-guided graph convolution networks.Communications biology · 2026Article
- Integrative Analysis of Glycosylation-Related Genes Reveals Prognostic Subtypes, Immune Evasion, and Therapeutic Vulnerabilities in Lung Adenocarcinoma.Oncology research · 2026Article
- Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.Frontiers in big data · 2025Review
- A Review of the Applications, Benefits, and Challenges of Generative AI for Sustainable Toxicology.Current research in toxicology · 2025Review
- Predictive Modeling of Anticancer Drug Sensitivity Using REFINED CNN.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Comparative evaluation of feature reduction methods for drug response prediction.Scientific reports · 2024Article
- Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.Briefings in bioinformatics · 2024Article
- Representing mutations for predicting cancer drug response.Bioinformatics (Oxford, England) · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Text-mining-based feature selection for anticancer drug response prediction.Bioinformatics advances · 2024Article
- Review
- A performance evaluation of drug response prediction models for individual drugs.Scientific reports · 2023Article
- NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning.Nature communications · 2023Article
- Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches.Frontiers in bioengineering and biotechnology · 2023Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug sensitivity prediction constitutes one of the main challenges in personalized medicine. Critically, the sensitivity of cancer cells to treatment depends on an unknown subset of a large number of biological features. Here, we compare standard, data-driven feature selection approaches to feature selection driven by prior knowledge of drug targets, target pathways, and gene expression signatures. We asses these methodologies on Genomics of Drug Sensitivity in Cancer (GDSC) dataset, evaluating 2484 unique models. For 23 drugs, better predictive performance is achieved when the features are selected according to prior knowledge of drug targets and pathways. The best correlation of observed and predicted response using the test set is achieved for Linifanib (r = 0.75). Extending the drug-dependent features with gene expression signatures yields the most predictive models for 60 drugs, with the best performing example of Dabrafenib. For many compounds, even a very small subset of drug-related features is highly predictive of drug sensitivity. Small feature sets selected using prior knowledge are more predictive for drugs targeting specific genes and pathways, while models with wider feature sets perform better for drugs affecting general cellular mechanisms. Appropriate feature selection strategies facilitate the development of interpretable models that are indicative for therapy design.
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