ArticleMethods in molecular biology (Clifton, N.J.)2024
Modeling the Depth of Cellular Dormancy from RNA-Sequencing Data.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Quiescence Multiverse.Biomolecules · 2025Review
- Quiescence-Origin Senescence: A New Paradigm in Cellular Aging.Biomedicines · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput transcriptome RNA sequencing is a powerful tool for understanding dynamic biological processes. Here, we present a computational framework, implemented in an R package QDSWorkflow, to characterize heterogeneous cellular dormancy depth using RNA-sequencing data from bulk samples and single cells.
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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.