ArticleScientific reports2016
RAMPred: identifying the N(1)-methyladenosine sites in eukaryotic transcriptomes.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 60 citations in OpenAlex.
- Functional Prediction of Epitranscriptome.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Fusion_f5C-Pred: a dual-branch feature fusion framework for 5-formylcytosine modification sites prediction.BMC genomics · 2025Article
- MCLCBA: multi-view contrastive learning network for RNA methylation site prediction.BMC bioinformatics · 2025Article
- Multimodal zero-shot learning of previously unseen epitranscriptomes from RNA-seq data.Briefings in bioinformatics · 2025Article
- Domain-knowledge enabled ensemble learning of 5-formylcytosine (f5C) modification sites.Computational and structural biotechnology journal · 2024Article
- 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
- Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.International journal of molecular sciences · 2024Article
- m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.BioData mining · 2024Article
- MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction.BMC bioinformatics · 2024Article
- Prognostic value of RNA methylation-related genes in gastric adenocarcinoma based on bioinformatics.PeerJ · 2024Article
- EMDL_m6Am: identifying N6,2'-O-dimethyladenosine sites based on stacking ensemble deep learning.BMC bioinformatics · 2023Article
- Novel insight into RNA modifications in tumor immunity: Promising targets to prevent tumor immune escape.Innovation (Cambridge (Mass.)) · 2023Review
- Self-attention enabled deep learning of dihydrouridine (D) modification on mRNAs unveiled a distinct sequence signature from tRNAs.Molecular therapy. Nucleic acids · 2023Article
- Review
- 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
- Epigenetics: Roles and therapeutic implications of non-coding RNA modifications in human cancers.Molecular therapy. Nucleic acids · 2021Review
- RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis.Nucleic acids research · 2021Article
- Machine learning applications in RNA modification sites prediction.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
N(1)-methyladenosine (m(1)A) is a prominent RNA modification involved in many biological processes. Accurate identification of m(1)A site is invaluable for better understanding the biological functions of m(1)A. However, limitations in experimental methods preclude the progress towards the identification of m(1)A site. As an excellent complement of experimental methods, a support vector machine based-method called RAMPred is proposed to identify m(1)A sites in H. sapiens, M. musculus and S. cerevisiae genomes for the first time. In this method, RNA sequences are encoded by using nucleotide chemical property and nucleotide compositions. RAMPred achieves promising performances in jackknife tests, cross cell line tests and cross species tests, indicating that RAMPred holds very high potential to become a useful tool for identifying m(1)A sites. For the convenience of experimental scientists, a web-server based on the proposed model was constructed and could be freely accessible at http://lin.uestc.edu.cn/server/RAMPred.
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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.