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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-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: 66

Showing 1-25 of 66 citing articles:

BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Bioinformatics (2021) Vol. 37, Iss. 17, pp. 2556-2562
Closed Access | Times Cited: 138
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides
Phasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 112
iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides
Phasit Charoenkwan, Janchai Yana, Chanin Nantasenamat, et al.
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 12, pp. 6666-6678
Closed Access | Times Cited: 121
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: 114
iDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method
Phasit Charoenkwan, Sakawrat Kanthawong, Chanin Nantasenamat, et al.
Journal of Proteome Research (2020) Vol. 19, Iss. 10, pp. 4125-4136
Closed Access | Times Cited: 81
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: 76
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: 54
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
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
Towards a better prediction of subcellular location of long non-coding RNA
Zhao‐Yue Zhang, Zi‐Jie Sun, Yuhe Yang, et al.
Frontiers of Computer Science (2022) Vol. 16, Iss. 5
Closed Access | Times Cited: 35
Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique
Hasan Zulfiqar, Qin-Lai Huang, Hao Lv, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 3, pp. 1251-1251
Open Access | Times Cited: 31
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
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
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: 23
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
DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features
Zhaomin Yao, Fei Li, Weiming Xie, et al.
Computers in Biology and Medicine (2024) Vol. 171, pp. 108166-108166
Open Access | Times Cited: 6
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
Recent Progress of Machine Learning in Gene Therapy
Cassandra Hunt, Sandra K. Montgomery, Joshua William Berkenpas, et al.
Current Gene Therapy (2021) Vol. 22, Iss. 2, pp. 132-143
Closed Access | Times Cited: 30
Laplacian Regularized Sparse Representation Based Classifier for Identifying DNA N4-Methylcytosine Sites via L2,1/2-Matrix Norm
Yijie Ding, Wenying He, Jijun Tang, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 20, Iss. 1, pp. 500-511
Open Access | Times Cited: 28
MuLan-Methyl—multiple transformer-based language models for accurate DNA methylation prediction
Wenhuan Zeng, Anupam Gautam, Daniel H. Huson
GigaScience (2022) Vol. 12
Open Access | Times Cited: 21
Mouse4mC-BGRU: Deep learning for predicting DNA N4-methylcytosine sites in mouse genome
Junru Jin, Yingying Yu, Leyi Wei
Methods (2022) Vol. 204, pp. 258-262
Closed Access | Times Cited: 20
Sequence-Based Intelligent Model for Identification of Tumor T Cell Antigens Using Fusion Features
N. Bibi, Mukhtaj Khan, Salman Khan, et al.
IEEE Access (2024) Vol. 12, pp. 155040-155051
Open Access | Times Cited: 4
In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review
Phasit Charoenkwan, Nuttapat Anuwongcharoen, Chanin Nantasenamat, et al.
Current Pharmaceutical Design (2020) Vol. 27, Iss. 18, pp. 2180-2188
Closed Access | Times Cited: 30
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