ArticlePeerJ2023
Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.
Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
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Who cites it
4 citing papers in PubMed.
- Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis.NPJ digital medicine · 2026Article
- DeepKinome: quantitative prediction of kinase binding affinity by a compound using deep learning based regression model.Frontiers in molecular biosciences · 2025Article
- Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC
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Registered trials
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