ArticlePloS one2014
An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models.iScience · 2025Review
- BANDRP: a bilinear attention network for anti-cancer drug response prediction based on fingerprint and multi-omics.Briefings in bioinformatics · 2024Article
- Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2024Article
- Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.Scientific reports · 2024Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction.Bioinformatics (Oxford, England) · 2021Article
- Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.PLoS computational biology · 2021Article
- An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression.Molecular therapy. Nucleic acids · 2020Article
- Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies.International journal of molecular sciences · 2020Article
- Ensembled machine learning framework for drug sensitivity prediction.IET systems biology · 2020Article
- Recursive model for dose-time responses in pharmacological studies.BMC bioinformatics · 2019Article
- Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma.BMC cancer · 2019Article
- Functional random forest with applications in dose-response predictions.Scientific reports · 2019Article
- Prediction of drug synergy score using ensemble based differential evolution.IET systems biology · 2019Article
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- Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response.Frontiers in genetics · 2019Article
- Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties.PloS one · 2019Article
- Predicting tumor cell line response to drug pairs with deep learning.BMC bioinformatics · 2018Article
- Investigation of model stacking for drug sensitivity prediction.BMC bioinformatics · 2018Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.
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