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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:

iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties
Wei Chen, Hui Yang, Pengmian Feng, et al.
Bioinformatics (2017) Vol. 33, Iss. 22, pp. 3518-3523
Open Access | Times Cited: 266

Showing 1-25 of 266 citing articles:

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA
Quan Zou, Pengwei Xing, Leyi Wei, et al.
RNA (2018) Vol. 25, Iss. 2, pp. 205-218
Open Access | Times Cited: 471
iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC
Pengmian Feng, Hui Yang, Hui Ding, et al.
Genomics (2018) Vol. 111, Iss. 1, pp. 96-102
Open Access | Times Cited: 298
iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC
Zhendong Su, Yan Huang, Zhao‐Yue Zhang, et al.
Bioinformatics (2018) Vol. 34, Iss. 24, pp. 4196-4204
Open Access | Times Cited: 273
Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Medicinal Research Reviews (2020) Vol. 40, Iss. 4, pp. 1276-1314
Closed Access | Times Cited: 256
Survey of Machine Learning Techniques in Drug Discovery
Natalie Stephenson, Emily Shane, Jessica Chase, et al.
Current Drug Metabolism (2018) Vol. 20, Iss. 3, pp. 185-193
Closed Access | Times Cited: 211
Sequence clustering in bioinformatics: an empirical study
Quan Zou, Gang Lin, Xingpeng Jiang, et al.
Briefings in Bioinformatics (2018)
Closed Access | Times Cited: 209
iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators
Chao-Qin Feng, Zhao‐Yue Zhang, Xiaojuan Zhu, et al.
Bioinformatics (2018) Vol. 35, Iss. 9, pp. 1469-1477
Closed Access | Times Cited: 206
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
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
Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy
Balachandran Manavalan, Sathiyamoorthy Subramaniyam, Tae Hwan Shin, et al.
Journal of Proteome Research (2018) Vol. 17, Iss. 8, pp. 2715-2726
Closed Access | Times Cited: 192
Predicting protein structural classes for low-similarity sequences by evaluating different features
Xiaojuan Zhu, Chao-Qin Feng, Hong-Yan Lai, et al.
Knowledge-Based Systems (2018) Vol. 163, pp. 787-793
Closed Access | Times Cited: 192
iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition
Wei Chen, Hui Ding, Xu Zhou, et al.
Analytical Biochemistry (2018) Vol. 561-562, pp. 59-65
Closed Access | Times Cited: 187
PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine
Balachandran Manavalan, Tae Hwan Shin, Gwang Lee
Frontiers in Microbiology (2018) Vol. 9
Open Access | Times Cited: 182
iRNA-3typeA: Identifying Three Types of Modification at RNA’s Adenosine Sites
Wei Chen, Pengmian Feng, Hui Yang, et al.
Molecular Therapy — Nucleic Acids (2018) Vol. 11, pp. 468-474
Open Access | Times Cited: 181
Identify origin of replication inSaccharomyces cerevisiaeusing two-step feature selection technique
Fanny Dao, Hao Lv, Fang Wang, et al.
Bioinformatics (2018) Vol. 35, Iss. 12, pp. 2075-2083
Closed Access | Times Cited: 180
HBPred: a tool to identify growth hormone-binding proteins
Hua Tang, Ya-Wei Zhao, Ping Zou, et al.
International Journal of Biological Sciences (2018) Vol. 14, Iss. 8, pp. 957-964
Open Access | Times Cited: 172
Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species
Leyi Wei, Shasha Luan, Luís Augusto Eijy Nagai, et al.
Bioinformatics (2018) Vol. 35, Iss. 8, pp. 1326-1333
Closed Access | Times Cited: 169
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
Naturally occurring modified ribonucleosides
Phillip J. McCown, Agnieszka Ruszkowska, Charlotte N. Kunkler, et al.
Wiley Interdisciplinary Reviews - RNA (2020) Vol. 11, Iss. 5
Open Access | Times Cited: 158
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
4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction
Wenying He, Cangzhi Jia, Quan Zou
Bioinformatics (2018) Vol. 35, Iss. 4, pp. 593-601
Closed Access | Times Cited: 160
iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC
Hui Yang, Wang‐Ren Qiu, Guoqing Liu, et al.
International Journal of Biological Sciences (2018) Vol. 14, Iss. 8, pp. 883-891
Open Access | Times Cited: 142
iRNA-2OM: A Sequence-Based Predictor for Identifying 2′-O-Methylation Sites inHomo sapiens
Hui Yang, Hao Lv, Hui Ding, et al.
Journal of Computational Biology (2018) Vol. 25, Iss. 11, pp. 1266-1277
Open Access | Times Cited: 141
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
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