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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:

BERMP: a cross-species classifier for predicting m6A sites by integrating a deep learning algorithm and a random forest approach
Yu Huang, Ningning He, Yu Chen, et al.
International Journal of Biological Sciences (2018) Vol. 14, Iss. 12, pp. 1669-1677
Open Access | Times Cited: 96

Showing 1-25 of 96 citing articles:

The potential role of RNA N6-methyladenosine in Cancer progression
Tianyi Wang, Shan Kong, Mei Tao, et al.
Molecular Cancer (2020) Vol. 19, Iss. 1
Open Access | Times Cited: 779
An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP
Yue Bi, Dongxu Xiang, Zongyuan Ge, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 22, pp. 362-372
Open Access | Times Cited: 138
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: 68
Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences
Zhen Chen, Pei Zhao, Fuyi Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 5, pp. 1676-1696
Closed Access | Times Cited: 121
m7GHub: deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine (m7G) sites in human
Bowen Song, Yujiao Tang, Kunqi Chen, et al.
Bioinformatics (2020) Vol. 36, Iss. 11, pp. 3528-3536
Open Access | Times Cited: 105
iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC
Iman Nazari, Muhammad Tahir, Hilal Tayara, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 193, pp. 103811-103811
Closed Access | Times Cited: 97
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: 85
Advances in the profiling of N6-methyladenosine (m6A) modifications
Hongxiang Zheng, Xiansheng Zhang, Na Sui
Biotechnology Advances (2020) Vol. 45, pp. 107656-107656
Open Access | Times Cited: 79
Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning
Haodong Xu, Peilin Jia, Zhongming Zhao
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Open Access | Times Cited: 72
Recent advances in the plant epitranscriptome
Lisha Shen, Jinqi Ma, Ping Li, et al.
Genome biology (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 32
Novel insight into RNA modifications in tumor immunity: Promising targets to prevent tumor immune escape
Yuxin Kong, Jie Yu, Shengfang Ge, et al.
The Innovation (2023) Vol. 4, Iss. 4, pp. 100452-100452
Open Access | Times Cited: 24
MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction
Honglei Wang, Tao Huang, Dong Wang, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 7
A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae
Xiaolei Zhu, Jingjing He, Shihao Zhao, et al.
Briefings in Functional Genomics (2019)
Closed Access | Times Cited: 60
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
The functional roles, cross-talk and clinical implications of m6A modification and circRNA in hepatocellular carcinoma
Sha Qin, Yitao Mao, Chen Xue, et al.
International Journal of Biological Sciences (2021) Vol. 17, Iss. 12, pp. 3059-3079
Open Access | Times Cited: 41
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: 29
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
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
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: 33
Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning
Pablo Acera Mateos, You Zhou, Kathi Zarnack, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Open Access | Times Cited: 15
Circular RNAs with protein-coding ability in oncogenesis
Jiahui Cheng, Guangyue Li, Wenmeng Wang, et al.
Biochimica et Biophysica Acta (BBA) - Reviews on Cancer (2023) Vol. 1878, Iss. 4, pp. 188909-188909
Closed Access | Times Cited: 13
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
m6A Reader: Epitranscriptome Target Prediction and Functional Characterization of N6-Methyladenosine (m6A) Readers
Di Zhen, Yuxuan Wu, Yuxin Zhang, et al.
Frontiers in Cell and Developmental Biology (2020) Vol. 8
Open Access | Times Cited: 36
Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks
Yiming Zhao, Ningning He, Zhen Chen, et al.
IEEE Access (2020) Vol. 8, pp. 14244-14252
Open Access | Times Cited: 32
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