ArticleeLife2020
A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Fluorinated Chromone Derivatives: Synthesis, Theoretical Study, and Evaluation of Their Biological Potential.ACS omega · 2026Article
- BESTDR Enables Bayesian Quantification of Mechanism-Specific Drug Responses.Cancer research · 2026Article
- M2ara: unraveling metabolomic drug responses in whole-cell MALDI mass spectrometry bioassays.Bioinformatics (Oxford, England) · 2024Article
- Multi-output prediction of dose-response curves enables drug repositioning and biomarker discovery.NPJ precision oncology · 2024Article
- Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.Bioinformatics (Oxford, England) · 2024Article
- Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling.Cell reports methods · 2023Article
- CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose-response curves.Nature communications · 2023Article
- Growth-rate model predicts in vivo tumor response from in vitro data.CPT: pharmacometrics & systems pharmacology · 2022Article
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- New Pyrrole Derivatives as Promising Biological Agents: Design, Synthesis, Characterization, In Silico, and Cytotoxicity Evaluation.International journal of molecular sciences · 2022Article
- Non-parametric synergy modeling of chemical compounds with Gaussian processes.BMC bioinformatics · 2022Article
- Using mathematical modeling to estimate time-independent cancer chemotherapy efficacy parameters.In silico pharmacology · 2022Article
- Preclinical Drug Response Metric Based on Cellular Response Phenotype Provides Better Pharmacogenomic Variables with Phenotype Relevance.Pharmaceuticals (Basel, Switzerland) · 2021Article
- bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments.Briefings in bioinformatics · 2021Article
- A Methodological Framework to Discover Pharmacogenomic Interactions Based on Random Forests.Genes · 2021Article
- A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.
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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.