ArticleMolecular therapy. Nucleic acids2020
An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression.
Article in Molecular therapy. Nucleic acids, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- RUNX3 Expression Level Is Correlated with the Clinical and Pathological Characteristics in Endometrial Cancer: A Systematic Review and Meta-analysis.BioMed research international · 2021Pooled it
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- Exploring anticancer drug structures through vertex based resolving parameters.Scientific reports · 2026Article
- The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.iScience · 2026Article
- Predictive modeling of influenza strain drugs using temperature-based topological indices and regression analysis via multi-criteria decision making techniques.Scientific reports · 2026Article
- Integrative analysis of immune and microbial subtypes predicts immunotherapy response in stomach adenocarcinoma.Microbiology spectrum · 2026Article
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- Mechanism-aware and safety-validated AI for drug repurposing: a critical review and translational pharmacology framework.Frontiers in pharmacology · 2026Review
- Mechanism-informed machine learning for interpretable drug release prediction and virtual formulation intervention in polymeric long-acting injectables.Frontiers in medicine · 2026Article
- Discovery of drug candidate to inhibit bronchogenic carcinoma genes biomarkers based on drug repurposing.Discover oncology · 2025Article
- Machine learning analysis of oral solid dosage formulation solubility variations by adjusting pressure and temperature.Scientific reports · 2025Article
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 2025Article
- Multi criterion decision making analysis of hematologic cancer drugs via topological indices and physicochemical properties.Scientific reports · 2025Article
- Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.Briefings in bioinformatics · 2025Article
- The covalent docking software landscape: features and applications in drug design.Briefings in bioinformatics · 2025Review
16 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.
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