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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.

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Showing 1-25 of 96 citing articles:

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: 186
An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP
Yue Bi, Dongxu Xiang, Zongyuan Ge, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 22, pp. 362-372
Open Access | Times Cited: 138
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
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
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
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
pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
Kuo‐Chen Chou, Xiang Cheng, Xuan Xiao
Genomics (2018) Vol. 111, Iss. 6, pp. 1274-1282
Open Access | Times Cited: 92
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
Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
Kuo‐Chen Chou
Current Medicinal Chemistry (2019) Vol. 26, Iss. 26, pp. 4918-4943
Closed Access | Times Cited: 91
Histone citrullination: a new target for tumors
Dongwei Zhu, Yue Zhang, Shengjun Wang
Molecular Cancer (2021) Vol. 20, Iss. 1
Open Access | Times Cited: 87
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
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
iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model
Shahid Akbar, Ashfaq Ahmad, Maqsood Hayat, et al.
Computers in Biology and Medicine (2021) Vol. 137, pp. 104778-104778
Closed Access | Times Cited: 76
iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types
Xuan Xiao, Yutao Shao, Xiang Cheng, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 63
pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments
Yaser Daanial Khan, Mehreen Jamil, Waqar Hussain, et al.
Journal of Theoretical Biology (2018) Vol. 463, pp. 47-55
Closed Access | Times Cited: 77
RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule
Lei Zheng, Shenghui Huang, Nengjiang Mu, et al.
Database (2019) Vol. 2019
Open Access | Times Cited: 68
iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-steps Rule and Pseudo Components
Omar Barukab, Yaser Daanial Khan, Sher Afzal Khan, et al.
Current Genomics (2019) Vol. 20, Iss. 4, pp. 306-320
Open Access | Times Cited: 65
iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-steps Rule
Sarah Ilyas, Waqar Hussain, Adeel Ashraf, et al.
Current Genomics (2019) Vol. 20, Iss. 4, pp. 275-292
Open Access | Times Cited: 64
pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset
Kuo‐Chen Chou, Xiang Cheng, Xuan Xiao
Medicinal Chemistry (2018) Vol. 15, Iss. 5, pp. 472-485
Closed Access | Times Cited: 59
Optimization of serine phosphorylation prediction in proteins by comparing human engineered features and deep representations
Sheraz Naseer, Waqar Hussain, Yaser Daanial Khan, et al.
Analytical Biochemistry (2020) Vol. 615, pp. 114069-114069
Closed Access | Times Cited: 58
iPhosS(Deep)-PseAAC: Identify Phosphoserine Sites in Proteins using Deep Learning on General Pseudo Amino Acid Compositions via Modified 5-Steps Rule
Sheraz Naseer, Waqar Hussain, Yaser Daanial Khan, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020) Vol. 19, Iss. 3, pp. 1703-1714
Closed Access | Times Cited: 50
ProtoPred: Advancing Oncological Research Through Identification of Proto-Oncogene Proteins
Sharaf J. Malebary, Rabia Khan, Yaser Daanial Khan
IEEE Access (2021) Vol. 9, pp. 68788-68797
Open Access | Times Cited: 49
pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset
Xuan Xiao, Xiang Cheng, Gen-Qiang Chen, et al.
Medicinal Chemistry (2018) Vol. 15, Iss. 5, pp. 496-509
Closed Access | Times Cited: 58
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