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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:

i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome
Wei Chen, Hao Lv, Fulei Nie, et al.
Bioinformatics (2019) Vol. 35, Iss. 16, pp. 2796-2800
Closed Access | Times Cited: 196

Showing 1-25 of 196 citing articles:

Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification
Haoyu Zhang, Quan Zou, Ying Ju, et al.
Current Bioinformatics (2022) Vol. 17, Iss. 5, pp. 473-482
Closed Access | Times Cited: 298
Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 16, pp. 733-744
Open Access | Times Cited: 200
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides
Vinothini Boopathi, Sathiyamoorthy Subramaniyam, Adeel Malik, et al.
International Journal of Molecular Sciences (2019) Vol. 20, Iss. 8, pp. 1964-1964
Open Access | Times Cited: 168
iProEP: A Computational Predictor for Predicting Promoter
Hong-Yan Lai, Zhao‐Yue Zhang, Zhendong Su, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 17, pp. 337-346
Open Access | Times Cited: 153
SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 131-141
Open Access | Times Cited: 151
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: 73
m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation
Chunyan Ao, Xiucai Ye, Tetsuya Sakurai, et al.
BMC Biology (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 54
Evaluation of different computational methods on 5-methylcytosine sites identification
Hao Lv, Zimei Zhang, Shi-Hao Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 3, pp. 982-995
Closed Access | Times Cited: 142
Identification of hormone binding proteins based on machine learning methods
Jiu-Xin Tan, Shi-Hao Li, Zimei Zhang, et al.
Mathematical Biosciences & Engineering (2019) Vol. 16, Iss. 4, pp. 2466-2480
Open Access | Times Cited: 135
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
A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features
Zhibin Lv, Shunshan Jin, Hui Ding, et al.
Frontiers in Bioengineering and Biotechnology (2019) Vol. 7
Open Access | Times Cited: 107
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: 105
AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Computational and Structural Biotechnology Journal (2019) Vol. 17, pp. 972-981
Open Access | Times Cited: 95
iRNA-m7G: Identifying N7-methylguanosine Sites by Fusing Multiple Features
Wei Chen, Pengmian Feng, Xiaoming Song, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 269-274
Open Access | Times Cited: 95
iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule
Muhammad Tahir, Hilal Tayara, Kil To Chong
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 189, pp. 96-101
Closed Access | Times Cited: 91
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Cells (2019) Vol. 8, Iss. 11, pp. 1332-1332
Open Access | Times Cited: 91
iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks
Muhammad Tahir, Hilal Tayara, Kil To Chong
Molecular Therapy — Nucleic Acids (2019) Vol. 16, pp. 463-470
Open Access | Times Cited: 85
iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule
Nguyen Quoc Khanh Le
Molecular Genetics and Genomics (2019) Vol. 294, Iss. 5, pp. 1173-1182
Closed Access | Times Cited: 83
Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes
Nguyen Quoc Khanh Le, Quang‐Thai Ho
Methods (2021) Vol. 204, pp. 199-206
Closed Access | Times Cited: 82
iRNAD: a computational tool for identifying D modification sites in RNA sequence
Zhaochun Xu, Pengmian Feng, Hui Yang, et al.
Bioinformatics (2019) Vol. 35, Iss. 23, pp. 4922-4929
Closed Access | Times Cited: 81
iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features
Bin Liu, Kai Li
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 80-87
Open Access | Times Cited: 81
RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites
Zhibin Lv, Jun Zhang, Hui Ding, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 81
Incorporating Distance-Based Top-n-gram and Random Forest To Identify Electron Transport Proteins
Xiaoqing Ru, Lihong Li, Quan Zou
Journal of Proteome Research (2019) Vol. 18, Iss. 7, pp. 2931-2939
Closed Access | Times Cited: 79
SNNRice6mA: A Deep Learning Method for Predicting DNA N6-Methyladenine Sites in Rice Genome
Haitao Yu, Zhiming Dai
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 79
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: 78
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