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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:

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

Showing 1-25 of 298 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
LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
Cheng Chen, Qingmei Zhang, Qin Ma, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 191, pp. 54-64
Open Access | Times Cited: 232
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
iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites
Jiangning Song, Yanan Wang, Fuyi Li, et al.
Briefings in Bioinformatics (2018) Vol. 20, Iss. 2, pp. 638-658
Open Access | Times Cited: 204
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
iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach
Bin Liu, Kai Li, De-Shuang Huang, et al.
Bioinformatics (2018) Vol. 34, Iss. 22, pp. 3835-3842
Open Access | Times Cited: 198
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
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
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
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
Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome
Fuyi Li, Chen Li, Tatiana T. Marquez‐Lago, et al.
Bioinformatics (2018) Vol. 34, Iss. 24, pp. 4223-4231
Open Access | Times Cited: 159
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
iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding
Nguyen Quoc Khanh Le, Edward Kien Yee Yapp, Quang‐Thai Ho, et al.
Analytical Biochemistry (2019) Vol. 571, pp. 53-61
Closed Access | Times Cited: 141
iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
Shahid Akbar, Maqsood Hayat
Journal of Theoretical Biology (2018) Vol. 455, pp. 205-211
Closed Access | Times Cited: 140
SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins
Waqar Hussain, Yaser Daanial Khan, Nouman Rasool, et al.
Journal of Theoretical Biology (2019) Vol. 468, pp. 1-11
Closed Access | Times Cited: 136
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
SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins
Waqar Hussain, Yaser Daanial Khan, Nouman Rasool, et al.
Analytical Biochemistry (2018) Vol. 568, pp. 14-23
Closed Access | Times Cited: 122
pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC
Xuan Xiao, Xiang Cheng, Gen-Qiang Chen, et al.
Genomics (2018) Vol. 111, Iss. 4, pp. 886-892
Open Access | Times Cited: 115
DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites
Quanzhong Liu, Jin-Xiang Chen, Yanze Wang, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Open Access | Times Cited: 114
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
Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC
M. Fazli Sabooh, Nadeem Iqbal, Mukhtaj Khan, et al.
Journal of Theoretical Biology (2018) Vol. 452, pp. 1-9
Closed Access | Times Cited: 107
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