ArticleComputational and structural biotechnology journal2021
TS-m6A-DL: Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 45 citations in OpenAlex.
- Mke-resnet: a lightweight and interpretable deep learning framework for efficient RNA m6A site identification.BMC bioinformatics · 2026Article
- Cross-RNA transferable sequence representation learning for lncRNA m6A site detection via novel deep domain separation networks.Briefings in bioinformatics · 2025Article
- Block sparse Bayes-based fuzzy system for RNA N6-methyladenosine sites prediction.PLoS computational biology · 2025Article
- Interpretability-guided RNA NCommunications biology · 2025Article
- Multimodal zero-shot learning of previously unseen epitranscriptomes from RNA-seq data.Briefings in bioinformatics · 2025Article
- Exploring the methyl-verse: Dynamic interplay of epigenome and m6A epitranscriptome.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Article
- Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.Biology · 2024Review
- Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues.Bioinformatics (Oxford, England) · 2023Article
- Optimizing Hyperparameter Tuning in Machine Learning to Improve the Predictive Performance of Cross-Species N6-Methyladenosine Sites.ACS omega · 2023Article
- m6A-TSHub: Unveiling the Context-specific mGenomics, proteomics & bioinformatics · 2023Article
- Review
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- RNADSN: Transfer-Learning 5-Methyluridine (mInternational journal of molecular sciences · 2022Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.Nucleic acids research · 2022Article
- ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks.International journal of molecular sciences · 2022Article
- CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The most communal post-transcriptional modification, N6-methyladenosine (m6A), is associated with a number of crucial biological processes. The precise detection of m6A sites around the genome is critical for revealing its regulatory function and providing new insights into drug design. Although both experimental and computational models for detecting m6A sites have been introduced, but these conventional methods are laborious and expensive. Furthermore, only a handful of these models are capable of detecting m6A sites in various tissues. Therefore, a more generic and optimized computational method for detecting m6A sites in different tissues is required. In this paper, we proposed a universal model using a deep neural network (DNN) and named it TS-m6A-DL, which can classify m6A sites in several tissues of humans (
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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.