ArticleGenes2020
Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models.
Article in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.Briefings in bioinformatics · 2025Article
- Biomarker challenges in the pursuit of personalized neoadjuvant chemotherapy for muscle-invasive bladder cancer: conclusions from SWOG S1314.Translational andrology and urology · 2024Article
- Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models.Cancers · 2023Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images.Frontiers in medicine · 2023Article
- A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening.Nature machine intelligence · 2022Article
- Converting tabular data into images for deep learning with convolutional neural networks.Scientific reports · 2021Article
- Learning curves for drug response prediction in cancer cell lines.BMC bioinformatics · 2021Article
- Ensemble transfer learning for the prediction of anti-cancer drug response.Scientific reports · 2020Article
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Authors and funding
9 authors.
Funding
Abstract
The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted
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Registered trials
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