ArticleBMC bioinformatics2021
EDLm
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 28 citations in OpenAlex.
- Advanced deep learning strategies in nanopore RNA sequencing.RNA biology · 2026Review
- MCLCBA: multi-view contrastive learning network for RNA methylation site prediction.BMC bioinformatics · 2025Article
- Cross-RNA transferable sequence representation learning for lncRNA m6A site detection via novel deep domain separation networks.Briefings in bioinformatics · 2025Article
- Machine learning-augmented m6A-Seq analysis without a reference genome.Briefings in bioinformatics · 2025Article
- Genome language modeling (GLM): a beginner's cheat sheet.Biology methods & protocols · 2025Article
- Hybrid representation learning for human mBioinformatics advances · 2025Article
- RNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.Briefings in bioinformatics · 2024Article
- MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction.BMC bioinformatics · 2024Article
- Analysis approaches for the identification and prediction ofEpigenetics · 2023Review
- EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework.Computational and structural biotechnology journal · 2023Article
- 4acCPred: Weakly supervised prediction ofMolecular therapy. Nucleic acids · 2022Article
- 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
- Artificial intelligence: A powerful paradigm for scientific research.Innovation (Cambridge (Mass.)) · 2021Review
- Machine learning applications in RNA modification sites prediction.Computational and structural biotechnology journal · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAs a common and abundant RNA methylation modification, N6-methyladenosine (m
resultsThis paper mainly studies feature extraction and classification of m
conclusionsOur proposed EDLm
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.