ArticleGenes2021
DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Computational identification and characterization of noncoding RNA-encoded peptides: tools, databases, and in silico strategies.Amino acids · 2026Review
- Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.PloS one · 2025Article
- Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.Biology · 2024Review
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs.Molecules (Basel, Switzerland) · 2024Article
- Review
- Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy.Frontiers in microbiology · 2023Article
- Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties.International journal of molecular sciences · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
- Staem5: A novel computational approachfor accurate prediction of m5C site.Molecular therapy. Nucleic acids · 2021Article
- M6A-BiNP: predicting NRNA biology · 2021Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC),
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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.