ArticleBMC bioinformatics2018
Predicting tumor cell line response to drug pairs with deep learning.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- Monotherapy cancer drug-blind response prediction is limited to intraclass generalization.PLoS computational biology · 2026Article
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.Research square · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.Bioinformatics advances · 2026Article
- Single-Nucleus RNA Sequencing Reveals Mid-Gestational Neurodevelopment Features in the Superior Temporal Plane from Fetuses with Nonsyndromic Cleft Lip and Palate.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.Scientific reports · 2025Article
- Multi-modal contrastive drug synergy prediction model guided by single modality.Journal of cheminformatics · 2025Article
- Herb-drug interactions in oncology: pharmacodynamic/pharmacokinetic mechanisms and risk prediction.Chinese medicine · 2025Review
- Prediction of cancer cell line-specific synergistic drug combinations based on multi-omics data.PeerJ · 2025Article
- Improving synergistic drug combination prediction with signature-based gene expression features in oncology.Frontiers in pharmacology · 2025Article
- A drug response prediction method for single-cell tumors combining attention networks and transfer learning.Frontiers in medicine · 2025Article
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.Journal of chemical information and modeling · 2024Article
- Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.Scientific reports · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- Combination therapy synergism prediction for virus treatment using machine learning models.PloS one · 2024Article
- Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach.BMC bioinformatics · 2023Article
- SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundThe National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.
resultsWe present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity.
conclusionsWe present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features.
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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.