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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.

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Showing 14 citing articles:

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning
Leping Liu, Lu Lu, Qiangqiang Zhao, et al.
Frontiers in Cell and Developmental Biology (2021) Vol. 9
Open Access | Times Cited: 37
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
PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence
Yan Wang, Shiwen Tai, Shuangquan Zhang, et al.
Genes (2023) Vol. 14, Iss. 7, pp. 1441-1441
Open Access | Times Cited: 9
iProm-Zea: A two-layer model to identify plant promoters and their types using convolutional neural network
Jeehong Kim, Muhammad Shujaat, Hilal Tayara
Genomics (2022) Vol. 114, Iss. 3, pp. 110384-110384
Open Access | Times Cited: 12
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
m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features
Zhongxing Xu, Xuan Wang, Jia Meng, et al.
Frontiers in Microbiology (2023) Vol. 14
Open Access | Times Cited: 7
Identification of 6-methyladenosine sites using novel feature encoding methods and ensemble models
Nashwan Alromema, Muhammad Taseer Suleman, Sharaf J. Malebary, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2′-O-methylation sites in human RNA
Md. Harun-Or-Roshid, Nhat Truong Pham, Balachandran Manavalan, et al.
PLoS ONE (2024) Vol. 19, Iss. 6, pp. e0305406-e0305406
Open Access | Times Cited: 2
Advances in detecting N6-methyladenosine modification in circRNAs
Lixia Ma, Lina He, Shiyang Kang, et al.
Methods (2022) Vol. 205, pp. 234-246
Closed Access | Times Cited: 7
Dynamic regulation and key roles of ribonucleic acid methylation
Jia Zou, Hui Liu, Wei Tan, et al.
Frontiers in Cellular Neuroscience (2022) Vol. 16
Open Access | Times Cited: 7
M6ATMR: identifying N6-methyladenosine sites through RNA sequence similarity matrix reconstruction guided by Transformer
Shuang Xiang, Te Zhang, Ming‐Hao Wu
PeerJ (2023) Vol. 11, pp. e15899-e15899
Open Access | Times Cited: 2
Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites
Zhengtao Luo, Liyi Yu, Zhaochun Xu, et al.
Biology (2024) Vol. 13, Iss. 10, pp. 777-777
Open Access
Machine learning classification models for the patients who have heart failure
Şevval Tuğçe BADİK
Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi (2023), pp. 235-244
Open Access
Recent Development of Computational Methods in the Field of Epitranscriptomics
Zijie Zhang, Shun Liu, Chuan He, et al.
Springer handbooks of computational statistics (2022), pp. 285-309
Closed Access
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