ArticleMolecular therapy. Nucleic acids2018
Anti-cancer Drug Response Prediction Using Neighbor-Based Collaborative Filtering with Global Effect Removal.
Article in Molecular therapy. Nucleic acids, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Multiomics-Driven Drug-Cell Interaction Network for Chemotherapy Sensitivity Prediction in Metabolically Defined Triple-Negative Breast Cancer Subtypes.Journal of cellular and molecular medicine · 2025Article
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
- BANDRP: a bilinear attention network for anti-cancer drug response prediction based on fingerprint and multi-omics.Briefings in bioinformatics · 2024Article
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Cancer drug response prediction with surrogate modeling-based graph neural architecture search.Bioinformatics (Oxford, England) · 2023Article
- DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.Molecules (Basel, Switzerland) · 2023Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- Drug response prediction using graph representation learning and Laplacian feature selection.BMC bioinformatics · 2022Article
- Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method.Scientific reports · 2022Article
- Integrated Drug Mining Reveals Actionable Strategies Inhibiting Plexiform Neurofibromas.Brain sciences · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Machine learning methods, databases and tools for drug combination prediction.Briefings in bioinformatics · 2022Article
- Machine learning approaches for drug combination therapies.Briefings in bioinformatics · 2021Review
- Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.Biomedicines · 2021Article
- Explainable drug sensitivity prediction through cancer pathway enrichment.Scientific reports · 2021Article
- Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model.BMC bioinformatics · 2021Article
- Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches.Briefings in bioinformatics · 2021Article
- A Random Walk with Restart Model Based on Common Neighbors for Predicting the Clinical Drug Combinations on Coronary Heart Disease.Journal of healthcare engineering · 2021Article
- Deep learning of pharmacogenomics resources: moving towards precision oncology.Briefings in bioinformatics · 2020Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients of the same cancer may differ in their responses to a specific medical therapy. Identification of predictive molecular features for drug sensitivity holds the key in the era of precision medicine. Human cell lines have harbored most of the same genetic changes found in patients' tumors and thus are widely used in the research of drug response. In this work, we formulated drug-response prediction as a recommender system problem and then adopted a neighbor-based collaborative filtering with global effect removal (NCFGER) method to estimate anti-cancer drug responses of cell lines by integrating cell-line similarity networks and drug similarity networks based on the fact that similar cell lines and similar drugs exhibit similar responses. Specifically, we removed the global effect in the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. We then used the K most similar neighbors (hybrid of cell-line-oriented and drug-oriented) in the available responses to predict the unknown ones. Through 10-fold cross-validation, this approach was shown to reach accurate and reproducible outcomes of drug sensitivity. We also discussed the biological outcomes based on the newly predicted response values.
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