ArticleGenome research2024
Delineating yeast cleavage and polyadenylation signals using deep learning.
Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Mechanisms of transcription termination across the coding and noncoding loci of the genome.Nature reviews. Molecular cell biology · 2026Review
- Regulation of gene expression by alternative polyadenylation in health and disease.Nature reviews. Genetics · 2026Review
- Review
- The Advances in Deep Learning Modeling of Polyadenylation Codes.Wiley interdisciplinary reviews. RNAReview
Corrections and comments
- Update of
Authors and funding
2 authors.
Funding
Abstract
3'-end cleavage and polyadenylation is an essential process for eukaryotic mRNA maturation. In yeast species, the polyadenylation signals that recruit the processing machinery are degenerate and remain poorly characterized compared with the well-defined regulatory elements in mammals. Here we address this issue by developing deep learning models to deconvolute degenerate
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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.