ArticleBiostatistics (Oxford, England)2022
Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach.
Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 3 citations in OpenAlex.
- CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.Bioinformatics advances · 2026Article
- Automated computer vision and dose-response modeling improve throughput and accuracy of an ex vivo functional precision medicine platform.Scientific reports · 2025Article
- BayesCurveFit: Enhancing Curve Fitting for Early-Stage Compound Screening Using Bayesian Inference.bioRxiv : the preprint server for biology · 2025Article
- Automated Computer Vision and Dose-Response Modeling Improve Throughput and Accuracy of anResearch square · 2025Article
- Review
- Improving drug response prediction via integrating gene relationships with deep learning.Briefings in bioinformatics · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- SynBa: improved estimation of drug combination synergies with uncertainty quantification.Bioinformatics (Oxford, England) · 2023Article
- Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach.BMC bioinformatics · 2023Article
- bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments.Briefings in bioinformatics · 2021Article
- A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response.PLoS computational biology · 2021Article
- A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates.eLife · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology. As genomic tumor profiling is becoming more common, targeted treatments for specific molecular alterations are gaining traction. To discover new potential therapeutics that may apply to broad classes of tumors matching some molecular pattern, experimentalists and pharmacologists rely on high-throughput, in vitro screens of many compounds against many different cell lines. We propose a hierarchical Bayesian model of how cancer cell lines respond to drugs in these experiments and develop a method for fitting the model to real-world high-throughput screening data. Through a case study, the model is shown to capture nontrivial associations between molecular features and drug response, such as requiring both wild type TP53 and overexpression of MDM2 to be sensitive to Nutlin-3(a). In quantitative benchmarks, the model outperforms a standard approach in biology, with $\approx20\%$ lower predictive error on held out data. When combined with a conditional randomization testing procedure, the model discovers markers of therapeutic response that recapitulate known biology and suggest new avenues for investigation. All code for the article is publicly available at https://github.com/tansey/deep-dose-response.
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What OpenQuestion holds
Registered trials
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.