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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:

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC
Pengmian Feng, Hui Ding, Hui Yang, et al.
Molecular Therapy — Nucleic Acids (2017) Vol. 7, pp. 155-163
Open Access | Times Cited: 285

Showing 1-25 of 285 citing articles:

iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC
Bin Liu, Fan Yang, De-Shuang Huang, et al.
Bioinformatics (2017) Vol. 34, Iss. 1, pp. 33-40
Open Access | Times Cited: 318
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
Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences
Bin Liu, Hao Wu, Kuo‐Chen Chou
Natural Science (2017) Vol. 09, Iss. 04, pp. 67-91
Open Access | Times Cited: 252
MLACP: machine-learning-based prediction of anticancer peptides
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Oncotarget (2017) Vol. 8, Iss. 44, pp. 77121-77136
Open Access | Times Cited: 242
pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Genomics (2017) Vol. 110, Iss. 1, pp. 50-58
Open Access | Times Cited: 228
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
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
WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach
Kunqi Chen, Zhen Wei, Qing Zhang, et al.
Nucleic Acids Research (2019) Vol. 47, Iss. 7, pp. e41-e41
Open Access | Times Cited: 199
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
AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest
Balachandran Manavalan, Tae Hwan Shin, Myeong O. Kim, et al.
Frontiers in Pharmacology (2018) Vol. 9
Open Access | Times Cited: 194
pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Molecular BioSystems (2017) Vol. 13, Iss. 9, pp. 1722-1727
Closed Access | Times Cited: 188
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
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
Protein acetylation sites with complex-valued polynomial model
Wenzheng Bao, Bin Yang
Frontiers of Computer Science (2024) Vol. 18, Iss. 3
Closed Access | Times Cited: 26
Detection, molecular function and mechanisms of m5C in cancer
Linhui Zhang, Yuelong Li, Liqing Li, et al.
Clinical and Translational Medicine (2025) Vol. 15, Iss. 3
Open Access | Times Cited: 2
pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Genomics (2017) Vol. 110, Iss. 4, pp. 231-239
Open Access | Times Cited: 165
iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Genomics (2017) Vol. 110, Iss. 5, pp. 239-246
Closed Access | Times Cited: 160
pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Gene (2017) Vol. 628, pp. 315-321
Closed Access | Times Cited: 159
pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Bioinformatics (2017) Vol. 34, Iss. 9, pp. 1448-1456
Open Access | Times Cited: 159
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
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