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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(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

Showing 1-25 of 187 citing articles:

The potential role of RNA N6-methyladenosine in Cancer progression
Tianyi Wang, Shan Kong, Mei Tao, et al.
Molecular Cancer (2020) Vol. 19, Iss. 1
Open Access | Times Cited: 800
Emerging role of tumor-related functional peptides encoded by lncRNA and circRNA
Pan Wu, Yongzhen Mo, Peng Miao, et al.
Molecular Cancer (2020) Vol. 19, Iss. 1
Open Access | Times Cited: 470
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
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
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: 111
Towards retraining-free RNA modification prediction with incremental learning
Jianbo Qiao, Junru Jin, Haoqing Yu, et al.
Information Sciences (2024) Vol. 660, pp. 120105-120105
Closed Access | Times Cited: 19
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
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
Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences
Zhen Chen, Pei Zhao, Fuyi Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 5, pp. 1676-1696
Closed Access | Times Cited: 122
iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC
Jianhua Jia, Xiaoyan Li, Wang‐Ren Qiu, et al.
Journal of Theoretical Biology (2018) Vol. 460, pp. 195-203
Closed Access | Times Cited: 105
iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components
Muhammad Tahir, Hilal Tayara, Kil To Chong
Journal of Theoretical Biology (2018) Vol. 465, pp. 1-6
Open Access | Times Cited: 101
iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC
Iman Nazari, Muhammad Tahir, Hilal Tayara, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 193, pp. 103811-103811
Closed Access | Times Cited: 97
iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition
Muhammad Awais, Waqar Hussain, Yaser Daanial Khan, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019) Vol. 18, Iss. 2, pp. 596-610
Closed 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
Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications
Zitao Song, Daiyun Huang, Bowen Song, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 90
iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition
Xuan Xiao, Zhaochun Xu, Wang‐Ren Qiu, et al.
Genomics (2018) Vol. 111, Iss. 6, pp. 1785-1793
Open Access | Times Cited: 88
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
Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components
Zhe Ju, Shiyun Wang
Genomics (2019) Vol. 112, Iss. 1, pp. 859-866
Closed Access | Times Cited: 85
MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou’s Five-Step Rule
Xiuquan Du, Yanyu Diao, Heng Liu, et al.
Journal of Proteome Research (2019) Vol. 18, Iss. 8, pp. 3119-3132
Closed Access | Times Cited: 84
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
Progresses in Predicting Post-translational Modification
Kuo‐Chen Chou
International Journal of Peptide Research and Therapeutics (2019) Vol. 26, Iss. 2, pp. 873-888
Closed Access | Times Cited: 82
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
DeepM6ASeq-EL: prediction of human N6-methyladenosine (m6A) sites with LSTM and ensemble learning
Juntao Chen, Quan Zou, Jing Li
Frontiers of Computer Science (2021) Vol. 16, Iss. 2
Closed Access | Times Cited: 80
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
Epigenetics: Roles and therapeutic implications of non-coding RNA modifications in human cancers
Dawei Rong, Guangshun Sun, Fan Wu, et al.
Molecular Therapy — Nucleic Acids (2021) Vol. 25, pp. 67-82
Open Access | Times Cited: 70
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