ReviewBriefings in bioinformatics2019
Evaluating the consistency of large-scale pharmacogenomic studies.
Review in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- BiDRA: Bayesian inference as a robust alternative to non-linear regression for dose-response efficiency metrics assessment.Bioinformatics advances · 2026Article
- Advances and Challenges in Drug Screening for Cancer Therapy: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2025Review
- A multi-omic machine learning approach deconstructs the role of amino acid metabolism in the immune microenvironment and prognosis of colon adenocarcinoma.Frontiers in immunology · 2025Article
- Article
- Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome.Frontiers in pharmacology · 2024Article
- Leveraging multi-source data to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Reassessing pharmacogenomic cell sensitivity with multilevel statistical models.Biostatistics (Oxford, England) · 2023Article
- Development of machine learning classifiers to predict compound activity on prostate cancer cell lines.Journal of cheminformatics · 2022Article
- NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.BMC medicine · 2022Article
- A cross-study analysis of drug response prediction in cancer cell lines.Briefings in bioinformatics · 2022Article
- The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients.Frontiers in genetics · 2022Article
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.
Funding
Abstract
Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data.
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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.