Cited by 179RNA modifications and cancerMachine Learning in BioinformaticsRNA regulation and disease

The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

Open access

Wei, Leyi, Chen, Huangrong, Su, Ran·2018·Molecular Therapy Nucleic Acids

DOI 10.1016/j.omtn.2018.07.004PMC PMC6082921

Open this paper in CoChat — free account Open PDF

Abstract

N6-methyladenosine (m 6 A) modification is the most abundant RNA methylation modification and involves various biological processes, such as RNA splicing and degradation. Recent studies have demonstrated the feasibility of identifying m 6 A peaks using high-throughput sequencing techniques. However, such techniques cannot accurately identify specific methylated sites, which is important for a better understanding of m 6 A functions. In this study, we develop a novel machine learning-based predictor called M6APred-EL for the identification of m 6 A sites. To predict m 6 A sites accurately within genomic sequences, we trained an ensemble of three support vector machine classifiers that explore the position-specific information and physical chemical information from position-specific k-mer nucleotide propensity, physical-chemical properties, and ring-function-hydrogen-chemical properties. We examined and compared the performance of our predictor with other state-of-the-art methods of benchmarking datasets. Comparative results showed that the proposed M6APred-EL performed more accurately for m 6 A site identification. Moreover, a user-friendly web server that implements the proposed M6APred-EL is well established and is currently available at http://server.malab.cn/M6APred-EL/. It is expected to be a practical and effective tool for the investigation of m 6 A functional mechanisms.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.omtn.2018.07.004

Cites

70 of 70 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq
Dominissini, D., Moshitch-Moshkovitz, S., Schwartz, S., Salmon-Divon, M., Ungar, L., Osenberg, S., Cesarkas, K., Jacob-Hirsch, J., Amariglio, N., Kupiec, M., et al. (2012) · Nature · doi:10.1038/nature11112 · Cited by 4,884
Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons
Meyer, K. D., Saletore, Y., Zumbo, P., Elemento, O., Mason, C. E., & Jaffrey, S. R. (2012) · Cell · doi:10.1016/j.cell.2012.05.003 · Cited by 4,223
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,359
Suppression of RNA Recognition by Toll-like Receptors: The Impact of Nucleoside Modification and the Evolutionary Origin of RNA
Karikó, K., Buckstein, M., Ni, H., & Weissman, D. (2005) · Immunity · doi:10.1016/j.immuni.2005.06.008 · Cited by 2,728
Improved free-energy parameters for predictions of RNA duplex stability.
Freier, S. M., Kierzek, R., Jaeger, J. A., Sugimoto, N., Caruthers, M. H., Neilson, T., & Turner, D. H. (1986) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.83.24.9373 · Cited by 1,470
SRAMP: prediction of mammalian N 6 -methyladenosine (m 6 A) sites based on sequence-derived features
Zhou, Y., Zeng, P., Li, Y., Zhang, Z., & Cui, Q. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw104 · Cited by 1,114
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences
Liu, B., Liu, F., Wang, X., Chen, J., Fang, L., & Chou, K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv458 · Cited by 723
Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing
Dominissini, D., Moshitch-Moshkovitz, S., Salmon‐Divon, M., Amariglio, N., & Rechavi, G. (2013) · Nature Protocols · doi:10.1038/nprot.2012.148 · Cited by 713
High-Resolution Mapping Reveals a Conserved, Widespread, Dynamic mRNA Methylation Program in Yeast Meiosis
Schwartz, S., Agarwala, S. D., Mumbach, M. R., Jovanović, M., Mertins, P., Shishkin, A., Tabach, Y., Mikkelsen, T. S., Satija, R., Ruvkun, G., et al. (2013) · Cell · doi:10.1016/j.cell.2013.10.047 · Cited by 627

Flagged first, then by citations

Show all 70 references

Sorted by citations

Items 101–179 of 179

iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool
Yang, X., Ye, X., Li, X., & Wei, L. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.663572 · Cited by 15
Self-citation
m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information
Wang, Y., Guo, R., Huang, L., Yang, S., Hu, X., & He, K. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.670852 · Cited by 15
Distant metastasis identification based on optimized graph representation of gene interaction patterns
Su, R., Zhu, Y., Zou, Q., & Wei, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab468 · Cited by 15
Self-citation
Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning
Luo, Z., Lou, L., Qiu, W., Xu, Z., & Xiao, X. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232415490 · Cited by 16
i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites
Xue, T., Zhang, S., & Qiao, H. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00429-4 · Cited by 14
A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol
Cai, J., Wang, D., Chen, R., Niu, Y., Ye, X., Su, R., Xiao, G., & Wei, L. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00502 · Cited by 13
Self-citation
Crosstalk between m6A and coding/non-coding RNA in cancer and detection methods of m6A modification residues
Meng, Q., Schatten, H., Zhou, Q., & Chen, J. (2023) · Aging · doi:10.18632/aging.204836 · Cited by 12
A bijective inference network for interpretable identification of RNA N 6 -methyladenosine modification sites
Li, G., Yang, Y., Li, D., Su, X., Zeng, Z., Hu, P., & Hu, L. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111541 · Cited by 12
RNA‐binding proteins and RNA methylation in myeloid cells
Bataclan, M., Leoni, C., & Monticelli, S. (2021) · Immunological Reviews · doi:10.1111/imr.13025 · Cited by 11
Bioinformatic tools for epitranscriptomics
Taguchi, Y. (2022) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00437.2022 · Cited by 12
Data fusion-based algorithm for predicting miRNA–Disease associations
Wang, C., Sun, K., Wang, J., & Guo, M. (2020) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2020.107357 · Cited by 13
Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues
Huang, G., Huang, X., & Jiang, J. (2024) · Methods · doi:10.1016/j.ymeth.2024.03.004 · Cited by 11
MST-m6A: A Novel Multi-Scale Transformer-based Framework for Accurate Prediction of m6A Modification Sites Across Diverse Cellular Contexts
Su, Q., Phan, L. T., Pham, N. T., Wei, L., & Manavalan, B. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168856 · Cited by 11
Self-citation
A Computational Method for the Identification of Endolysins and Autolysins
Xu, L., Liang, G., Chen, B., Tan, X., Xiang, H., & Liao, C. (2019) · Protein and Peptide Letters · doi:10.2174/0929866526666191002104735 · Cited by 11
Identifying RNA N6-Methyladenine Sites in Three Species Based on a Markov Model
Pian, C., Yang, Z., Yang, Y., Zhang, L., & Chen, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.650803 · Cited by 11
CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction
Zhang, G., Luo, W., Lyu, J., Yu, Z., & Huang, G. (2022) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00500-0 · Cited by 11
4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism
Zeng, R., Cheng, S., & Liao, M. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.664669 · Cited by 10
Identification of Pathogenetic Brain Regions via Neuroimaging Data for Diagnosis of Autism Spectrum Disorders
Wang, Y., Fu, Y., & Luo, X. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.900330 · Cited by 10
Review of Progress in Predicting Protein Methylation Sites
Ao, C., Jin, S., Lin, Y., & Zou, Q. (2019) · Current Organic Chemistry · doi:10.2174/1385272823666190723141347 · Cited by 10
RMNet: An RNA m6A Cross-species Methylation Detection Method for Nanopore Sequencing
Li, Q., Sun, C., Wang, D., & Lou, J. (2025) · Current Drug Targets · doi:10.2174/0113894501405283250627072052 · Cited by 9
Interpretability-guided RNA N6-methyladenosine modification site prediction with invertible neural networks
Li, G., Su, X., Yang, Y., Li, D., Cui, Z., Deng, X., Hu, P., & Hu, L. (2025) · Communications Biology · doi:10.1038/s42003-025-08265-8 · Cited by 9
Advances in detecting N6-methyladenosine modification in circRNAs
Ma, L., He, L., Kang, S., Gu, B., Gao, S., & Zuo, Z. (2022) · Methods · doi:10.1016/j.ymeth.2022.07.011 · Cited by 9
Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences
Wang, R., Chung, C., Huang, H., & Lee, T.-Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac573 · Cited by 9
bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network
Islam, N. U., & Park, J. (2021) · Genes · doi:10.3390/genes12081155 · Cited by 9
6mAPred-MSFF: A Deep Learning Model for Predicting DNA N6-Methyladenine Sites across Species Based on a Multi-Scale Feature Fusion Mechanism
Zeng, R., & Liao, M. (2021) · Applied Sciences · doi:10.3390/app11167731 · Cited by 9
Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies
Guo, Y., Wu, C., Yuan, Z., Wang, Y., Liang, Z., Wang, Y., Zhang, Y., & Xu, L. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.801113 · Cited by 8
Domain-knowledge enabled ensemble learning of 5-formylcytosine (f5C) modification sites
Huang, J., Wang, X., Xia, R. M., Yang, D., Liu, J., Lv, Q., Yu, X., Meng, J., Chen, K., Song, B., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.08.004 · Cited by 8
Post-transcriptional regulation of IFI16 promotes inflammatory endothelial pathophenotypes observed in pulmonary arterial hypertension
Rao, R. J., Yang, J., Jiang, S., El-Khoury, W., Hafeez, N., Okawa, S., Tai, Y., Tang, Y. M., Aaraj, Y. A., Sembrat, J., et al. (2024) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00048.2024 · Cited by 8
Bioluminescent Proteins Prediction with Voting Strategy
Zhao, S., Ju, Y., Ye, X., Zhang, J., & Han, S. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200601122328 · Cited by 8
GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome
Cai, J., Xiao, G., & Su, R. (2022) · Methods · doi:10.1016/j.ymeth.2022.02.001 · Cited by 8
Self-citation
DP-AOP: A novel SVM-based antioxidant proteins identifier
Meng, C., Pei, Y., Zou, Q., & Yuan, L. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.125499 · Cited by 8
CWLy-RF: A novel approach for identifying cell wall lyases based on random forest classifier
Jiao, S., Xu, L., & Ju, Y. (2021) · Genomics · doi:10.1016/j.ygeno.2021.06.038 · Cited by 9
Better Performance with Transformer: CPPFormer in the Precise Prediction of Cell-penetrating Peptides
Xue, Y., Ye, X., Wei, L., Zhang, X., Sakurai, T., & Wei, L. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210920103140 · Cited by 8
Self-citation
SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles
Zhang, Z., Gong, Y., Gao, B., Li, H., Gao, W., Zhao, Y., & Dong, B. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.809001 · Cited by 8
A review of methods for predicting DNA N6-methyladenine sites
Han, K., Wang, J., Wang, Y., Zhang, L., Yu, M., Xie, F., Zheng, D., Xu, Y., Ding, Y., & Wan, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac514 · Cited by 8
Prediction of Thermophilic Proteins Using Voting Algorithm
Li, J., Zhu, P., & Zou, Q. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17938-0_18 · Cited by 7
Accurate Prediction and Key Feature Recognition of Immunoglobulin
Gong, Y., Liao, B., Peng, D., & Zou, Q. (2021) · Applied Sciences · doi:10.3390/app11156894 · Cited by 7
A Feature Fusion Predictor for RNA Pseudouridine Sites with Particle Swarm Optimizer Based Feature Selection and Ensemble Learning Approach
Wang, X. R., Lin, X., Wang, R., Han, N., Fan, K., Han, L., & Ding, Z. (2021) · Current Issues in Molecular Biology · doi:10.3390/cimb43030129 · Cited by 7
Tissue specific prediction of N6-methyladenine sites based on an ensemble of multi-input hybrid neural network
Jia, C., JIN, D., Wang, X., & Zhao, Q. (2021) · Biocell · doi:10.32604/biocell.2022.016655 · Cited by 7
MTTLm6A: A multi-task transfer learning approach for base-resolution mRNA m6A site prediction based on an improved transformer
Wang, H., Zeng, W., Huang, X., Liu, Z., Sun, Y., & Zhang, L. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024013 · Cited by 6
Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species
Wang, R., Chung, C., & Lee, T.-Y. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25052869 · Cited by 6
The role of polypeptides encoded by ncRNAs in cancer
Huang, J., Yang, P., Pan, W., Wu, F. H., Qiu, J., & Ma, Z. (2024) · Gene · doi:10.1016/j.gene.2024.148817 · Cited by 6
N6-methyladenosine and its epitranscriptomic effects on hematopoietic stem cell regulation and leukemogenesis
Chang, K.-J., Shiau, L.-Y., Lin, S.-C., Cheong, H.-P., Wang, C., Ma, C., Liang, Y., Yang, Y., Ko, P., Hsu, C.-H., et al. (2024) · Molecular Medicine · doi:10.1186/s10020-024-00965-x · Cited by 6
Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies
Guo, Y., Cheng, H., Yuan, Z., Liang, Z., Wang, Y., & Du, D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.801261 · Cited by 6
Integrating LASSO Feature Selection and Soft Voting Classifier to Identify Origins of Replication Sites
Zhang, S., Yao, Y., & Xue, T. (2022) · Current Genomics · doi:10.2174/1389202923666220214122506 · Cited by 6
MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites of Saccharomyces cerevisiae
Wang, H., Zhao, S., Cheng, Y., Bi, S., & Zhu, X. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.999506 · Cited by 6
Progress in the Development of Antimicrobial Peptide Prediction Tools
Ao, C., Zhang, Y., Li, D., Zhao, Y., & Zou, Q. (2020) · Current Protein and Peptide Science · doi:10.2174/1389203721666200117163802 · Cited by 5
Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM
Zhao, S., Pan, Q., Zou, Q., Ju, Y., Shi, L., & Su, X. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/7518779 · Cited by 5
An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm
Hamdy, W., Ismail, A., Awad, W. A., Ibrahim, A. H., & Hassanien, A. E. (2023) · Sensors · doi:10.3390/s23042219 · Cited by 5
Predicting Sub-Golgi Apparatus Resident Protein With Primary Sequence Hybrid Features
Wang, C., Li, J., Liu, X., & Guo, M. (2019) · IEEE Access · doi:10.1109/access.2019.2962821 · Cited by 4
Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction
Zhu, W. Q., Guo, Y., & Zou, Q. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021297 · Cited by 4
A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods
Yang, Y. R., Wang, J.-S., Yuan, S.-S., Liu, M., Su, W., Lin, H., & Zhang, Z. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210910125802 · Cited by 4
The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning
Chen, Z., Jiao, S., Da, Z., Zou, Q., Xu, L., Zhang, L., & Su, X. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.845622 · Cited by 4
Progress of m6A Methylation in Lipid Metabolism in Humans and Animals
Xin, Z., Zhang, T., Lu, Q., Yang, Z., & Chen, Z. (2022) · Agriculture · doi:10.3390/agriculture12101683 · Cited by 4
Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion
Zhao, S., Zhang, Y., Ding, Y., Zou, Q., Tang, L., Liu, Q., & Zhang, Y. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.015 · Cited by 4
Using Chou’s 5-steps rule to identify N6-methyladenine sites by ensemble learning combined with multiple feature extraction methods
Zhang, Z., & Wang, L. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1821778 · Cited by 4
SICM6A: Identifying m 6 A Site across Species by Transposed GRU NetworkPreprint
Liu, W. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/694158 · Cited by 4
Computational models for prediction of m6A sites using deep learning
Sheng, N., Qiao, J., Wei, L., Shi, H. W., Guo, H., & Yang, C. (2025) · Methods · doi:10.1016/j.ymeth.2025.04.011 · Cited by 3
Self-citation
Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding
Yang, Y., Zheng, H., Wang, C., Xiao, W., & Liu, T. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20092344 · Cited by 3
Prediction of Sphingosine protein-coding regions with a self adaptive spectral rotation method
Li, Z., Guan, Y., Yuan, X., Zheng, P., & Zhu, H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214442 · Cited by 3
DNA/RNA sequence feature representation algorithms for predicting methylation-modified sites
Xie, J., Wang, M., & Xu, S. (2022) · Scientia Sinica Vitae · doi:10.1360/ssv-2022-0074 · Cited by 3
A computational method for small molecule-RNA binding sites identification by utilizing position specificity and complex network information
Zhang, L., Xiao, K., & Kong, L. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.105094 · Cited by 2
Multi-scale DNA language model improves 6 mA binding sites prediction
Hou, A., Luo, H., Huan, L., Luo, L., & Ding, P. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108129 · Cited by 2
Capturing short-range and long-range dependencies of nucleotides for identifying RNA N6-methyladenosine modification sites
Li, G., Zhao, B.-W., Su, X., Yang, Y., Zeng, Z., Hu, P., & Hu, L. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109625 · Cited by 2
Experimental Approaches and Computational Workflows for Systematic Mapping and Functional Interpretation of RNA Modifications
Mir, Q., & Janga, S. C. (2021) · RNA technologies · doi:10.1007/978-3-030-71612-7_7 · Cited by 2
Identification and Prediction of Key Nucleotide Sites Using Machine Learning in Bioinformatics: A Brief Overview
Cai, J., Wei, L., Zeng, K., & Xiao, G. (2019) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00170 · Cited by 1
Self-citation
Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification
Bortoletto, E., & Rosani, U. (2024) · Genes · doi:10.3390/genes15080996 · Cited by 1
Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites
Luo, Z., Yu, L., Xu, Z., Liu, K., & Gu, L. (2024) · Biology · doi:10.3390/biology13100777 · Cited by 2
Mke-resnet: a lightweight and interpretable deep learning framework for efficient RNA m6A site identification
Gao, X., Jia, J., Hui, C., & Lin, Y. (2026) · BMC Bioinformatics · doi:10.1186/s12859-026-06416-0 · Cited by 1
Peptides from the Shadows: Molecular Depth Insights and Clinical Horizons of Circular RNA-encoded Peptides in Gliomas
Rejili, M., Farahani, N., & Alimohammadi, M. (2026) · NeuroMolecular Medicine · doi:10.1007/s12017-026-08931-y · Cited by 1
Recent Development of Computational Methods in the Field of Epitranscriptomics
Zhang, Z. S., Liu, S., He, C., & Chen, M. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_15 · Cited by 1
iR6mA-RNN: Identifying N6-Methyladenosine Sites in Eukaryotic Transcriptomes using Recurrent Neural Networks and Sequence-embedded Features
Nguyen, B. P., Nguyen‐Vo, T., Nguyen, L., Trinh, Q. H., Baliuag, C., Do, T. T. T., & Rahardja, S. (2023) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp53291.2023.10207989 · Cited by 1
Dm6A-TSVM: detection of N6-methyladenosine (m6A) sites from RNA transcriptomes using the twin support vector machines
Liu, Z., Fu, K., Yin, H., Xia, K., Xiao, Y., Wang, H., & Li, G. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02409-w · Cited by 1
tRNA is a molecular thermometer of species' optimal growth temperature
Temesgen, S. A., Grace-Mercure, B. K., Ahmad, B., Fida, H., Lin, H., Liu, L., & YANG, K. Y. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.146849
Prediction of RNA m6A Methylation Sites in Multiple Tissues Based on Dual-branch Residual Network
Xiao-Tian, G. U. O., Gao, W., Chen, D., Hui-Min, L. I., & Xue-Wen, T. A. N. (2025) · PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS · doi:10.3724/j.pibb.2025.0167
Additional file 1 of iTTCA-RF: a random forest predictor for tumor T cell antigens
Jiao, S., Zou, Q., Guo, H., & Shi, L. (2021) · Figshare · doi:10.6084/m9.figshare.16893085
DT-m6A: A DenseNet–Transformer Hybrid Framework for Accurate Prediction of m6A Modification Sites across Diverse Cell Lines and Tissues
Tao, Q., & Jia, J. (2026) · Frontiers in Bioscience-Landmark · doi:10.31083/fbl48029
MLA-ac4C: A N4-Acetylcytidine Site Prediction Model Based on Multi- Layer Attention, Bidirectional GRU, and DenseNet
Jia, J., Li, G., & Sun, M. (2026) · Current Genomics · doi:10.2174/0113892029407030251229080710
Integrative interpretable learning reveals shared patterns of epitranscriptomic regulation across multiple cancer types
Yin, X., Tu, G., Wang, X., Liu, Y., Wang, Y., Ma, J., Yu, X., Meng, J., & Song, B. (2026) · BMC Biology · doi:10.1186/s12915-026-02628-1

Authors: Wei, L., Chen, H., & Su, R.
Affiliations: Tianjin University (CN)
Corresponding author: Su, Ran
ORCID: Wei, Leyi
Su, Ran
Venue: Molecular Therapy Nucleic Acids
Publisher: Elsevier BV
Published: 2018-09
Volume: 12
Pages: 635-644
ISSN: 2162-2531 (ISSN-L)
DOI: 10.1016/j.omtn.2018.07.004
PMC: PMC6082921
OpenAlex: W2813360597
Cited by: 179
References: 70
Citations by year: 2026: 5 · 2025: 8 · 2024: 11 · 2023: 14 · 2022: 28 · 2021: 37 · 2020: 29 · 2019: 40 · 2018: 7
FWCI: 10.05
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Molecular Therapy — Nucleic Acids (publishedVersion, cc-by-nc-nd)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Natural Science Foundation of China (61701340, 61702361)
State Key Laboratory of Medicinal Chemical Biology
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA modifications and cancer
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
Keywords: N6-methyladenosine, RNA methylation, m6A site prediction, ensemble learning, support vector machine, physicochemical properties, web server, RNA modification

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks