ArticleGenomics2021
EditPredict: Prediction of RNA editable sites with convolutional neural network.
Article in Genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Structure-aware graph learning predicts RNA editability across tissues and species.Research square · 2026Article
- ADAR-GPT: Learning the language of the epitranscriptome.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- ADAR-GPT: A continually fine-tuned language model for predicting A-to-I RNA editing sites.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events.Briefings in bioinformatics · 2025Article
- DEMINING: A deep learning model embedded framework to distinguish RNA editing from DNA mutations in RNA sequencing data.Genome biology · 2024Article
- Review
- Article
- ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species.Briefings in bioinformatics · 2023Article
- Deep learning in CRISPR-Cas systems: a review of recent studies.Frontiers in bioengineering and biotechnology · 2023Review
- A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to predict human RNA editing events in both Alu regions and non-Alu regions. With a validation dataset resulting from CRISPR/Cas9 knockout of the ADAR1 enzyme, the validation accuracies reached 99.5% and 93.6% for Alu and non-Alu regions, respectively. We ported our CNN models in a web service named EditPredict. EditPredict not only works on reference genome sequences but can also take into consideration single nucleotide variants in personal genomes. In addition to the human genome, EditPredict tackles other model organisms including bumblebee, fruitfly, mouse, and squid genomes. EditPredict can be used stand-alone to predict novel RNA editing and it can be used to assist in filtering for candidate RNA editing detected from RNA-Seq data.
Indexed as
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.