ArticlePlant molecular biology2018
RFAthM6A: a new tool for predicting m
Article in Plant molecular biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed, 70 citations in OpenAlex.
- Article
- Multi-kernel feature extraction with dynamic fusion and downsampled residual feature embedding for predicting rice RNA N6-methyladenine sites.Briefings in bioinformatics · 2024Article
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.International journal of molecular sciences · 2024Article
- PRMD: an integrated database for plant RNA modifications.Nucleic acids research · 2024Article
- The expanding role of RNA modifications in plant RNA polymerase II transcripts: highlights and perspectives.Journal of experimental botany · 2023Review
- Review
- Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.Briefings in bioinformatics · 2023Review
- MethSemble-6mA: an ensemble-based 6mA prediction server and its application on promoter region of LBD gene family in Poaceae.Frontiers in plant science · 2023Article
- Article
- EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction.BMC bioinformatics · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites ofFrontiers in microbiology · 2022Article
- Using k-mer embeddings learned from a Skip-gram based neural network for building a cross-species DNA N6-methyladenine site prediction model.Plant molecular biology · 2021Article
- M6A-BiNP: predicting NRNA biology · 2021Article
- Epigenetics: Roles and therapeutic implications of non-coding RNA modifications in human cancers.Molecular therapy. Nucleic acids · 2021Review
- Article
- Article
- Machine learning applications in RNA modification sites prediction.Computational and structural biotechnology journal · 2021Review
- m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information.Frontiers in genetics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
key messageWe curated a reliable dataset of m
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.