ArticleNature communications2025
Comprehensive discovery of m
Article in Nature communications, 2025. 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
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
3 citing papers in PubMed.
- MethyNano: supervised contrastive pretraining enables robust and generalizable methylation detection from nanopore sequencing.Bioinformatics (Oxford, England) · 2026Article
- LncRNAs in Ovarian Cancer: Emerging Insights and Future Perspectives in Tumor Biology and Clinical Applications.Cancers · 2026Review
- Comprehensive discovery of mNature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
RNA modifications (RMs) are critical for diverse biological processes, but the lack of accurate, quantitative detection methods has limited their study. A large-scale and high-quality training dataset is an essential component for accurate deep learning, but such dataset has been absent for RM detection, resulting in low accuracies. We developed DeepRM (Deep learning for RNA Modification), a sophisticated deep learning framework powered by Nanopore sequencing. DeepRM dataset is a massive-scale, three orders of magnitude larger than the comparable previous ones, and unprecedentedly high-quality dataset that closely mirrors endogenous transcript environments. Accordingly, DeepRM detects RM sites and measures their modification stoichiometries with a near-perfect accuracy. Using DeepRM, we constructed a comprehensive, human m
Indexed as
Identifiers
What Socratic 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 Socratic graph.