ArticleiScience2023
Random Forest model reveals the interaction between N6-methyladenosine modifications and RNA-binding proteins.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Integrative transcriptomic and machine learning analysis identifies candidate biomarkers and immune features in diabetic ulcers.Molecular biology reports · 2026Article
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- Reprogramming patient-induced pluripotent stem cell-specific retinal organoids for deciphering epigenetic modifications of RNA methylation.Journal of the Chinese Medical Association : JCMA · 2025Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
RNA-binding proteins (RBPs) have critical roles in N6-methyladenosine (m6A) modification process. We designed a Random Forest (RF) model to systematically analyze the interaction among RBPs and m6A modifications by integrating the binding signals from hundreds of RBPs. Accurate prediction of m6A sites demonstrated significant connections between RBP bindings and m6A modifications. The relative importance of different RBPs from the model provided a quantitative metric to evaluate their interactions with m6A modifications. Redundancy analysis showed that several RBPs may have similar binding patterns with m6A sites. The RF model exhibited fairly high prediction accuracy across cell lines, suggesting a conservative RBP interaction network regulates m6A occupancy. Specific RBPs can engage to the corresponding regional m6A sites and deploy distinct regulatory processes, such as cleavage site selection of the alternative polyadenylation (APA). We also integrated histone modifications into our RF model, which demonstrated H3K36me3 and H3K27me3 as determining features for m6A distribution.
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Registered trials
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