ArticleNPJ systems biology and applications2019
Defining subpopulations of differential drug response to reveal novel target populations.
Article in NPJ systems biology and applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 26 citations in OpenAlex.
- Genomic landscape of drug binding and pharmacogenetic variation across diverse populations using SNPdrug3D.Nature communications · 2026Article
- Genetic Variation in Drug Targets: Are We Ready for the Era of Precision Medicinal Chemistry?ACS medicinal chemistry letters · 2025Article
- scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data.BMC bioinformatics · 2024Article
- A Narrative Review on the Application of Large Language Models to Support Cancer Care and Research.Yearbook of medical informatics · 2024Review
- CREAMMIST: an integrative probabilistic database for cancer drug response prediction.Nucleic acids research · 2023Article
- Review
- Molecular targeted therapy for anticancer treatment.Experimental & molecular medicine · 2022Review
- Network Dynamics Caused by Genomic Alteration Determine the Therapeutic Response to FGFR Inhibitors for Lung Cancer.Biomolecules · 2022Article
- The value of real world evidence: The case of medical cannabis.Frontiers in psychiatry · 2022Review
- Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era.Current oncology (Toronto, Ont.) · 2021Review
- Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.International journal of molecular sciences · 2021Review
- 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
- Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.Cancers · 2020Review
- Integrated Computational Approaches and Tools forAllosteric Drug Discovery.International journal of molecular sciences · 2020Review
- Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.American journal of cancer research · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personalised medicine has predominantly focused on genetically altered cancer genes that stratify drug responses, but there is a need to objectively evaluate differential pharmacology patterns at a subpopulation level. Here, we introduce an approach based on unsupervised machine learning to compare the pharmacological response relationships between 327 pairs of cancer therapies. This approach integrated multiple measures of response to identify subpopulations that react differently to inhibitors of the same or different targets to understand mechanisms of resistance and pathway cross-talk. MEK, BRAF, and PI3K inhibitors were shown to be effective as combination therapies for particular
Indexed as
Identifiers
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.