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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:

Computational identification of N6-methyladenosine sites in multiple tissues of mammals
Fanny Dao, Hao Lv, Yuhe R. Yang, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 1084-1091
Open Access | Times Cited: 84

Showing 1-25 of 84 citing articles:

Anticancer peptides prediction with deep representation learning features
Zhibin Lv, Feifei Cui, Quan Zou, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 119
Biological Sequence Classification: A Review on Data and General Methods
Chunyan Ao, Shihu Jiao, Yansu Wang, et al.
Research (2022) Vol. 2022
Open Access | Times Cited: 73
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
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework
Leyi Wei, Wenjia He, Adeel Malik, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 114
Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications
Zitao Song, Daiyun Huang, Bowen Song, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 90
AI applications in functional genomics
Claudia Caudai, Antonella Galizia, Filippo Geraci, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 5762-5790
Open Access | Times Cited: 82
AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins
Fanny Dao, Menglu Liu, Wei Su, et al.
International Journal of Biological Macromolecules (2022) Vol. 228, pp. 706-714
Closed Access | Times Cited: 48
Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions
Lei Chen, Zhandong Li, Shiqi Zhang, et al.
BioMed Research International (2022) Vol. 2022, pp. 1-11
Open Access | Times Cited: 39
High-Accuracy Identification and Structure–Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry
Wanxing Li, Xuejing Liu, Yuanfa Liu, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access | Times Cited: 1
Identification of sub-Golgi protein localization by use of deep representation learning features
Zhibin Lv, Pingping Wang, Quan Zou, et al.
Bioinformatics (2020) Vol. 36, Iss. 24, pp. 5600-5609
Open Access | Times Cited: 55
Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli
Hasan Zulfiqar, Zi‐Jie Sun, Qin-Lai Huang, et al.
Methods (2021) Vol. 203, pp. 558-563
Closed Access | Times Cited: 54
Identification of cyclin protein using gradient boost decision tree algorithm
Hasan Zulfiqar, Shi-Shi Yuan, Qin-Lai Huang, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 4123-4131
Open Access | Times Cited: 51
DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes
Mobeen Ur Rehman, Hilal Tayara, Kil To Chong
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022) Vol. 20, Iss. 2, pp. 904-911
Closed Access | Times Cited: 32
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
Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics
Lian Liu, Bowen Song, Jiani Ma, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 1587-1604
Open Access | Times Cited: 49
im6A-TS-CNN: Identifying the N6-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network
Kewei Liu, Lei Cao, Pu-Feng Du, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 21, pp. 1044-1049
Open Access | Times Cited: 47
iEnhancer-EBLSTM: Identifying Enhancers and Strengths by Ensembles of Bidirectional Long Short-Term Memory
Kun Niu, Ximei Luo, Shumei Zhang, et al.
Frontiers in Genetics (2021) Vol. 12
Open Access | Times Cited: 35
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
m6A-NeuralTool: Convolution Neural Tool for RNA N6-Methyladenosine Site Identification in Different Species
Mobeen Ur Rehman, Jeehong Kim, Hilal Tayara, et al.
IEEE Access (2021) Vol. 9, pp. 17779-17786
Open Access | Times Cited: 33
N(6)-methyladenosine modification: A vital role of programmed cell death in myocardial ischemia/reperfusion injury
Jian Wang, Yanyan Li, Song Zhang
International Journal of Cardiology (2022) Vol. 367, pp. 11-19
Closed Access | Times Cited: 26
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
Critical evaluation of web-based DNA N6-methyladenine site prediction tools
Md Mehedi Hasan, Watshara Shoombuatong, Hiroyuki Kurata, et al.
Briefings in Functional Genomics (2020) Vol. 20, Iss. 4, pp. 258-272
Closed Access | Times Cited: 37
DLm6Am: A Deep-Learning-Based Tool for Identifying N6,2′-O-Dimethyladenosine Sites in RNA Sequences
Zhengtao Luo, Wei Su, Liliang Lou, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 19, pp. 11026-11026
Open Access | Times Cited: 21
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
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