ReviewTrends in cancer2021
Expanding and Remixing the Metadata Landscape.
Review in Trends in cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer.Cancer biology & therapy · 2021Pooled it
- Metappuccino: large language model-driven reconstruction of sequence read archive metadata for cancer research.Bioinformatics (Oxford, England) · 2026Article
- aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.Genome biology · 2025Article
- Gene signatures for cancer research: A 25-year retrospective and future avenues.PLoS computational biology · 2024Article
- Review
- Metadata retrieval from sequence databases with ffq.Bioinformatics (Oxford, England) · 2023Article
- Kmerator Suite: design of specificNAR genomics and bioinformatics · 2021Article
- Improving the completeness of public metadata accompanying omics studies.Genome biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Genomic data sharing accelerates research. Data are most valuable when they are accompanied by detailed metadata. To date, metadata are often human-annotated descriptions of samples and their handling. We discuss how machine learning-derived elements complement such descriptions to enhance the research ecosystem around genomic data.
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.