ArticleBMC cancer2015
Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.
Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 2 of them syntheses that pooled it.
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Who cites it
73 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.Cancer research and treatment · 2019Pooled it
- RLASON-CDR: a reinforcement learning-driven adaptive synergistic optimization network for cancer drug response prediction.Briefings in bioinformatics · 2026Article
- BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.BMC bioinformatics · 2026Article
- DVFGCDR: a dual-view fusion graph neural network for cancer drug response prediction.NAR cancer · 2025Article
- SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction.bioRxiv : the preprint server for biology · 2025Article
- MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction.BMC bioinformatics · 2025Article
- Use of artificial intelligence in healthcare in South Africa: A scoping review.Health SA = SA Gesondheid · 2025Review
- ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.PLoS computational biology · 2025Article
- Predictive Modeling of Anticancer Drug Sensitivity Using REFINED CNN.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction.Frontiers in genetics · 2025Article
- Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.Briefings in bioinformatics · 2024Article
- Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.Briefings in bioinformatics · 2024Article
- DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies.BMC bioinformatics · 2024Article
- Cancer drug sensitivity prediction from routine histology images.NPJ precision oncology · 2024Article
- Text-mining-based feature selection for anticancer drug response prediction.Bioinformatics advances · 2024Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- Review
- A performance evaluation of drug response prediction models for individual drugs.Scientific reports · 2023Article
- Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL.Genomics, proteomics & bioinformatics · 2023Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel.
methodsRecently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP).
resultsOur model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80% accuracy for 10 drugs, ≥75% accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively.
conclusionsThese results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.
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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.