ArticleJournal of computational biology : a journal of computational molecular cell biology2020
Explaining Gene Expression Using Twenty-One MicroRNAs.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
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
5 citing papers in PubMed, 5 citations in OpenAlex.
- Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data.bioRxiv : the preprint server for biology · 2026Article
- Article
- Transcriptome Complexity Disentangled: A Regulatory Molecules Approach.International journal of molecular sciences · 2025Article
- MiR-1270 Suppresses the Malignant Progression of Breast Cancer via Targeting MMD2.Journal of healthcare engineering · 2022Article
- miR-1207-5p Can Contribute to Dysregulation of Inflammatory Response in COVID-19Frontiers in cellular and infection microbiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The transcriptome of a tumor contains detailed information about the disease. Although advances in sequencing technologies have generated larger data sets, there are still many questions about exactly how the transcriptome is regulated. One class of regulatory elements consists of microRNAs (or miRs), many of which are known to be associated with cancer. To better understand the relationships between miRs and cancers, we analyzed ∼9000 samples from 32 cancer types studied in The Cancer Genome Atlas. Our feature reduction algorithm found evidence for 21 biologically interpretable clusters of miRs, many of which were statistically associated with a specific type of cancer. Moreover, the clusters contain sufficient information to distinguish between most types of cancer. We then used linear models to measure, genome-wide, how much variation in gene expression could be explained by the 21 average expression values ("scores") of the clusters. Based on the ∼20,000 per-gene
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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.