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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 1-25 of 82 citing articles:

Accurately identifying hemagglutinin using sequence information and machine learning methods
Xidan Zou, Liping Ren, Peiling Cai, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 68
Meta-i6mA: an interspecies predictor for identifying DNAN6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework
Md Mehedi Hasan, Shaherin Basith, Mst. Shamima Khatun, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 112
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: 110
iDNA-MS: An Integrated Computational Tool for Detecting DNA Modification Sites in Multiple Genomes
Hao Lv, Fanny Dao, Dan Zhang, et al.
iScience (2020) Vol. 23, Iss. 4, pp. 100991-100991
Open Access | Times Cited: 103
i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation
Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, et al.
Plant Molecular Biology (2020) Vol. 103, Iss. 1-2, pp. 225-234
Closed Access | Times Cited: 77
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning
Md Mehedi Hasan, Md. Ashad Alam, Watshara Shoombuatong, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 76
Prediction of bio-sequence modifications and the associations with diseases
Chunyan Ao, Liang Yu, Quan Zou
Briefings in Functional Genomics (2020) Vol. 20, Iss. 1, pp. 1-18
Closed Access | Times Cited: 72
Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy
Md Mehedi Hasan, Sho Tsukiyama, Jae Youl Cho, et al.
Molecular Therapy (2022) Vol. 30, Iss. 8, pp. 2856-2867
Open Access | Times Cited: 65
C-Loss Based Higher Order Fuzzy Inference Systems for Identifying DNA N4-Methylcytosine Sites
Yijie Ding, Prayag Tiwari, Quan Zou, et al.
IEEE Transactions on Fuzzy Systems (2022) Vol. 30, Iss. 11, pp. 4754-4765
Open Access | Times Cited: 37
i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes
Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 906-912
Open Access | Times Cited: 65
PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method
Phasit Charoenkwan, Sakawrat Kanthawong, Nalini Schaduangrat, et al.
Cells (2020) Vol. 9, Iss. 2, pp. 353-353
Open Access | Times Cited: 58
A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD
Zhiyu Tao, Yanjuan Li, Zhixia Teng, et al.
Computational and Mathematical Methods in Medicine (2020) Vol. 2020, pp. 1-9
Open Access | Times Cited: 57
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: 53
iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Analytical Biochemistry (2020) Vol. 599, pp. 113747-113747
Closed Access | Times Cited: 52
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: 50
A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features
Nguyen Quoc Khanh Le, Duyen Thi, Trinh‐Trung‐Duong Nguyen, et al.
Gene (2021) Vol. 787, pp. 145643-145643
Closed Access | Times Cited: 49
RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features
Chunyan Ao, Quan Zou, Liang Yu
Methods (2021) Vol. 203, pp. 32-39
Closed Access | Times Cited: 43
Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools
Balachandran Manavalan, Md Mehedi Hasan, Shaherin Basith, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 22, pp. 406-420
Open Access | Times Cited: 44
AptaNet as a deep learning approach for aptamer–protein interaction prediction
Neda Emami, Reza Ferdousi
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 39
DCNN-4mC: Densely connected neural network based N4-methylcytosine site prediction in multiple species
Mobeen Ur Rehman, Hilal Tayara, Kil To Chong
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 6009-6019
Open Access | Times Cited: 37
Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction
Ying Liang, Yanan Wu, Zequn Zhang, et al.
BMC Bioinformatics (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 22
Multi-correntropy fusion based fuzzy system for predicting DNA N4-methylcytosine sites
Yijie Ding, Prayag Tiwari, Fei Guo, et al.
Information Fusion (2023) Vol. 100, pp. 101911-101911
Open Access | Times Cited: 14
i6mA-stack: A stacking ensemble-based computational prediction of DNA N6-methyladenine (6mA) sites in the Rosaceae genome
Jhabindra Khanal, Dae Young Lim, Hilal Tayara, et al.
Genomics (2020) Vol. 113, Iss. 1, pp. 582-592
Open Access | Times Cited: 38
iAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides
Phasit Charoenkwan, Sakawrat Kanthawong, Chanin Nantasenamat, et al.
Genomics (2020) Vol. 113, Iss. 1, pp. 689-698
Open Access | Times Cited: 38
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
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