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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

TS-m6A-DL: Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model
Zeeshan Abbas, Hilal Tayara, Quan Zou, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 4619-4625
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering
Guodong Li, Bo-Wei Zhao, Xiaorui Su, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 4, pp. 2362-2372
Closed Access | Times Cited: 26
Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes
Nguyen Quoc Khanh Le, Quang‐Thai Ho
Methods (2021) Vol. 204, pp. 199-206
Closed Access | Times Cited: 82
In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets
Jianbo Liao, Qinyu Wang, Fengxu Wu, et al.
Molecules (2022) Vol. 27, Iss. 20, pp. 7103-7103
Open Access | Times Cited: 56
RNA structure prediction using deep learning — A comprehensive review
Mayank Chaturvedi, Mahmood A. Rashid, Kuldip K. Paliwal
Computers in Biology and Medicine (2025) Vol. 188, pp. 109845-109845
Open Access | Times Cited: 1
i6mA-Caps: a CapsuleNet-based framework for identifying DNA N6-methyladenine sites
Mobeen Ur Rehman, Hilal Tayara, Quan Zou, et al.
Bioinformatics (2022) Vol. 38, Iss. 16, pp. 3885-3891
Open Access | Times Cited: 34
m6A-TSHub: Unveiling the Context-Specific m6A Methylation and m6A-Affecting Mutations in 23 Human Tissues
Bowen Song, Daiyun Huang, Yuxin Zhang, et al.
Genomics Proteomics & Bioinformatics (2022) Vol. 21, Iss. 4, pp. 678-694
Open Access | Times Cited: 30
Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation
Daiyun Huang, Kunqi Chen, Bowen Song, et al.
Nucleic Acids Research (2022) Vol. 50, Iss. 18, pp. 10290-10310
Open Access | Times Cited: 24
Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues
Guohua Huang, Xiaohong Huang, Jinyun Jiang
Methods (2024) Vol. 226, pp. 1-8
Closed Access | Times Cited: 6
M6A-BERT-Stacking: A Tissue-Specific Predictor for Identifying RNA N6-Methyladenosine Sites Based on BERT and Stacking Strategy
Qianyue Li, Xin Cheng, Chen Song, et al.
Symmetry (2023) Vol. 15, Iss. 3, pp. 731-731
Open Access | Times Cited: 12
Multi-task adaptive pooling enabled synergetic learning of RNA modification across tissue, type and species from low-resolution epitranscriptomes
Yiyou Song, Yue Wang, Xuan Wang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Closed Access | Times Cited: 10
Using statistical analysis to explore the influencing factors of data imbalance for machine learning identification methods of human transcriptome m6A modification sites
Mingxin Li, Rujun Li, Yichi Zhang, et al.
Computational Biology and Chemistry (2025) Vol. 115, pp. 108351-108351
Closed Access
Injecting structure-aware insights for the learning of RNA sequence representations to identify m6A modification sites
Yue Yu, Shuang Xiang, Ming‐Hao Wu
PeerJ (2025) Vol. 13, pp. e18878-e18878
Open Access
A bijective inference network for interpretable identification of RNA N6-methyladenosine modification sites
Guodong Li, Yue Yang, Dongxu Li, et al.
Pattern Recognition (2025), pp. 111541-111541
Closed Access
Drugs inhibition prediction in P-gp enzyme: a comparative study of machine learning and graph neural network
Maryam Maryam, Mobeen Ur Rehman, Kil To Chong, et al.
Computational Toxicology (2025), pp. 100344-100344
Closed Access
Computational models for prediction of m6A sites using deep learning
Nan Sheng, Jianbo Qiao, Leyi Wei, et al.
Methods (2025)
Closed Access
ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks
Zeeshan Abbas, Hilal Tayara, Kil To Chong
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 15, pp. 8314-8314
Open Access | Times Cited: 16
Capturing short-range and long-range dependencies of nucleotides for identifying RNA N6-methyladenosine modification sites
Guodong Li, Bo-Wei Zhao, Xiaorui Su, et al.
Computers in Biology and Medicine (2025) Vol. 186, pp. 109625-109625
Closed Access
Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues
Ying Zhang, Zhikang Wang, Yiwen Zhang, et al.
Bioinformatics (2023) Vol. 39, Iss. 12
Open Access | Times Cited: 8
Crosstalk between m6A and coding/non-coding RNA in cancer and detection methods of m6A modification residues
Qingren Meng, Heide Schatten, Qian Zhou, et al.
Aging (2023) Vol. 15, Iss. 13, pp. 6577-6619
Open Access | Times Cited: 7
RNADSN: Transfer-Learning 5-Methyluridine (m5U) Modification on mRNAs from Common Features of tRNA
Zhirou Li, Jinge Mao, Daiyun Huang, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 21, pp. 13493-13493
Open Access | Times Cited: 11
GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network
Shi Qiu, Renxin Liu, Ying Liang
Computers in Biology and Medicine (2023) Vol. 163, pp. 107202-107202
Closed Access | Times Cited: 6
MST-m6A: A Novel Multi-Scale Transformer-based Framework for Accurate Prediction of m6A Modification Sites Across Diverse Cellular Contexts
Qiaosen Su, Le Thi Phan, Nhat Truong Pham, et al.
Journal of Molecular Biology (2024), pp. 168856-168856
Closed Access | Times Cited: 2
CNNLSTMac4CPred: A Hybrid Model for N4-Acetylcytidine Prediction
Guiyang Zhang, Wei Luo, Jianyi Lyu, et al.
Interdisciplinary Sciences Computational Life Sciences (2022) Vol. 14, Iss. 2, pp. 439-451
Closed Access | Times Cited: 9
m6A-TSHub: unveiling the context-specific m6A methylation and m6A-affecting mutations in 23 human tissues
Bowen Song, Daiyun Huang, Yuxin Zhang, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 8
Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning
Zhengtao Luo, Liliang Lou, Wang‐Ren Qiu, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 24, pp. 15490-15490
Open Access | Times Cited: 8
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