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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:

i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome
Md Mehedi Hasan, Balachandran Manavalan, Mst. Shamima Khatun, et al.
International Journal of Biological Macromolecules (2019) Vol. 157, pp. 752-758
Closed Access | Times Cited: 82

Showing 51-75 of 82 citing articles:

Identification of DNA N4-methylcytosine Sites via Multiview Kernel Sparse Representation Model
Chengwei Ai, Prayag Tiwari, Hongpeng Yang, et al.
IEEE Transactions on Artificial Intelligence (2022) Vol. 4, Iss. 5, pp. 1236-1245
Open Access | Times Cited: 8
iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome
Dae Yeong Lim, Mobeen Ur Rehman, Kil To Chong
Symmetry (2021) Vol. 13, Iss. 5, pp. 899-899
Open Access | Times Cited: 11
Fuzzy Neural Tangent Kernel Model for Identifying DNA N4-methylcytosine Sites
Yijie Ding, Prayag Tiwari, Fei Guo, et al.
IEEE Transactions on Fuzzy Systems (2024) Vol. 32, Iss. 10, pp. 5714-5727
Closed Access | Times Cited: 1
Voting-ac4C:Pre-trained large RNA language model enhances RNA N4-acetylcytidine site prediction
Yulian Jia, Zilong Zhang, Shankai Yan, et al.
International Journal of Biological Macromolecules (2024) Vol. 282, pp. 136940-136940
Closed Access | Times Cited: 1
Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites
Md Mamunur Rashid, Swakkhar Shatabda, Md Mehedi Hasan, et al.
Current Genomics (2020) Vol. 21, Iss. 3, pp. 194-203
Open Access | Times Cited: 10
PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations
Firda Nurul Auliah, Andi Nur Nilamyani, Watshara Shoombuatong, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 4, pp. 2120-2120
Open Access | Times Cited: 9
Review and Comparative Analysis of Machine Learning-based Predictors for Predicting and Analyzing Anti-angiogenic Peptides
Phasit Charoenkwan, Wararat Chiangjong, Md Mehedi Hasan, et al.
Current Medicinal Chemistry (2021) Vol. 29, Iss. 5, pp. 849-864
Closed Access | Times Cited: 9
A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4—Methylcytosine Using Deep Learning Approach
Rajib Kumar Halder, Mohammed Nasir Uddin, Md Ashraf Uddin, et al.
Genes (2023) Vol. 14, Iss. 3, pp. 582-582
Open Access | Times Cited: 3
IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations
Md Mehedi Hasan, Md. Ashad Alam, Watshara Shoombuatong, et al.
Journal of Computer-Aided Molecular Design (2021) Vol. 35, Iss. 3, pp. 315-323
Closed Access | Times Cited: 8
Weighted Fuzzy System for Identifying DNA N4-Methylcytosine Sites With Kernel Entropy Component Analysis
Leyao Wang, Prayag Tiwari, Yijie Ding, et al.
IEEE Transactions on Artificial Intelligence (2023) Vol. 5, Iss. 2, pp. 895-903
Closed Access | Times Cited: 3
The prediction of human DNase I hypersensitive sites based on DNA sequence information
Wei Su, Fang Wang, Jiu-Xin Tan, et al.
Chemometrics and Intelligent Laboratory Systems (2020) Vol. 209, pp. 104223-104223
Closed Access | Times Cited: 6
An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier
Samme Amena Tasmia, Fee Faysal Ahmed, Parvez Mosharaf, et al.
Current Genomics (2021) Vol. 22, Iss. 2, pp. 122-136
Open Access | Times Cited: 6
i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning
Yanjuan Li, Zhengnan Zhao, Zhixia Teng
BioMed Research International (2021) Vol. 2021, pp. 1-11
Open Access | Times Cited: 6
Particle Swarm Optimization-Assisted Multilayer Ensemble Model to predict DNA 4mC sites
Sajeeb Saha, Rajib Kumar Halder, Mohammed Nasir Uddin
Informatics in Medicine Unlocked (2023) Vol. 42, pp. 101374-101374
Open Access | Times Cited: 2
4mC-CGRU: Identification of N4-Methylcytosine (4mC) sites using convolution gated recurrent unit in Rosaceae genome
Abida Sultana, Sadia Jannat Mitu, Md Naimul Pathan, et al.
Computational Biology and Chemistry (2023) Vol. 107, pp. 107974-107974
Open Access | Times Cited: 2
PSP-PJMI: An innovative feature representation algorithm for identifying DNA N4-methylcytosine sites
Mingzhao Wang, Juanying Xie, P.W. Grant, et al.
Information Sciences (2022) Vol. 606, pp. 968-983
Closed Access | Times Cited: 4
Identification of DNA N4-methylcytosine sites via fuzzy model on self representation
Leyao Wang, Yijie Ding, Junhai Xu, et al.
Applied Soft Computing (2022) Vol. 122, pp. 108840-108840
Closed Access | Times Cited: 3
4acCPred: Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences
Jingxian Zhou, Xuan Wang, Zhen Wei, et al.
Molecular Therapy — Nucleic Acids (2022) Vol. 30, pp. 337-345
Open Access | Times Cited: 3
Recent Development of Machine Learning Methods in Sumoylation Sites Prediction
Yiwei Zhao, Shihua Zhang, Hui Ding
Current Medicinal Chemistry (2021) Vol. 29, Iss. 5, pp. 894-907
Closed Access | Times Cited: 4
An Effective Algorithm Based on Sequence and Property Information for N4-methylcytosine Identification in Multiple Species
Lichao Zhang, Xueting Wang, Kang Xiao, et al.
Letters in Organic Chemistry (2024) Vol. 21, Iss. 8, pp. 695-706
Closed Access
Mus4mCPred: Accurate Identification of DNA N4-Methylcytosine Sites in Mouse Genome Using Multi-View Feature Learning and Deep Hybrid Network
Xiao Wang, Qian Du, Rong Wang
Processes (2024) Vol. 12, Iss. 6, pp. 1129-1129
Open Access
A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods
Zheng-Xing Guan, Shi-Hao Li, Zimei Zhang, et al.
Current Genomics (2020) Vol. 21, Iss. 1, pp. 11-25
Open Access | Times Cited: 3
iAMY‐DC: Identifying Amyloid Proteins by Using Dynamic Correlation Features
Hongliang Zou
ChemistrySelect (2023) Vol. 8, Iss. 17
Closed Access | Times Cited: 1
Fast and Accurate Classification of Meta-Genomics Long Reads With deSAMBA
Gaoyang Li, Yongzhuang Liu, Deying Li, et al.
Frontiers in Cell and Developmental Biology (2021) Vol. 9
Open Access | Times Cited: 3
A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods
Yuhe R. Yang, Jia-Shu Wang, Shi-Shi Yuan, et al.
Current Medicinal Chemistry (2021) Vol. 29, Iss. 5, pp. 789-806
Closed Access | Times Cited: 3

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