ArticleBMC bioinformatics2023
Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- DeepSynBa: actionable drug combination prediction with complete dose-response profiles.Bioinformatics (Oxford, England) · 2026Article
- KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction.Journal of translational medicine · 2025Article
- An open-source screening platform accelerates discovery of drug combinations.Nature communications · 2025Article
- Combined Treatment of Sodium Butyrate and Bromelain Enhanced Anticancer Effects in Colorectal Cancer Cell Lines: A Promising Therapeutic Approach.International journal of molecular sciences · 2025Article
- Scaling up drug combination surface prediction.Briefings in bioinformatics · 2025Article
- Predicting drug combination response surfaces.npj drug discovery · 2025Article
- Multi-output prediction of dose-response curves enables drug repositioning and biomarker discovery.NPJ precision oncology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.
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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.