ArticleBioinformatics (Oxford, England)2016
rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE.PLoS computational biology · 2020Pooled it
- Integrated bioinformatics analyses and cell line experiments implicate miR-33a-5p/HIF1A in the cellular response to celastrol in lung adenocarcinoma.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- drGT: interpretable drug response prediction with attention-guided gene attribution on a drug-cell-gene heterogeneous graph.BMC bioinformatics · 2026Article
- Lung tumouroids as a testing platform for precision CAR T cell therapy.Nature biomedical engineering · 2026Article
- CellMiner cross-database (CellMinerCDB) version 2.2 for explorations of patient-derived cancer cell line pharmacogenomics.Nucleic acids research · 2026Article
- drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network.ArXiv · 2025Article
- Article
- DPH2 is a biomarker associated with cell death, immunity and prognosis based on pan-cancer analysis.Discover oncology · 2025Article
- CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations.Cancer research · 2023Article
- Monocyte subpopulation profiling indicates CDK6-derived cell differentiation and identifies subpopulation-specific miRNA expression sets in acute and stable coronary artery disease.Scientific reports · 2022Article
- TPWshiny: an interactive R/Shiny app to explore cell line transcriptional responses to anti-cancer drugs.Bioinformatics (Oxford, England) · 2022Article
- CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics.Nucleic acids research · 2021Article
- Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.Nature communications · 2020Article
- The Indenoisoquinoline LMP517: A Novel Antitumor Agent Targeting both TOP1 and TOP2.Molecular cancer therapeutics · 2020Article
- Mining Drug-Target Associations in Cancer: Analysis of Gene Expression and Drug Activity Correlations.Biomolecules · 2020Article
- Article
- FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines.Bioinformatics (Oxford, England) · 2019Article
- CellMinerCDB for Integrative Cross-Database Genomics and Pharmacogenomics Analyses of Cancer Cell Lines.iScience · 2018Article
- Novel Fluoroindenoisoquinoline Non-Camptothecin Topoisomerase I Inhibitors.Molecular cancer therapeutics · 2018Article
- Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links.BMC bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
purposeThe rcellminer R package provides a wide range of functionality to help R users access and explore molecular profiling and drug response data for the NCI-60. The package enables flexible programmatic access to CellMiner's unparalleled breadth of NCI-60 data, including gene and protein expression, copy number, whole exome mutations, as well as activity data for ∼21K compounds, with information on their structure, mechanism of action and repeat screens. Functions are available to easily visualize compound structures, activity patterns and molecular feature profiles. Additionally, embedded R Shiny applications allow interactive data exploration. AVAILABILITY AND IMPLEMENTATION: rcellminer is compatible with R 3.2 and above on Windows, Mac OS X and Linux. The package, documentation, tutorials and Shiny-based applications are available through Bioconductor (http://www.bioconductor.org/packages/rcellminer); ongoing updates will occur according to the Bioconductor release schedule with new CellMiner data. The package is free and open-source (LGPL 3). CONTACT: lunaa@cbio.mskcc.org or vinodh.rajapakse@nih.gov.
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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.