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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:

iPhos‐PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Molecular Informatics (2016) Vol. 36, Iss. 5-6
Closed Access | Times Cited: 154

Showing 26-50 of 154 citing articles:

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
Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types
Wei‐Zhong Lin, Dong Xu
Bioinformatics (2016) Vol. 32, Iss. 24, pp. 3745-3752
Open Access | Times Cited: 96
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
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
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
Therapeutic stapled peptides: Efficacy and molecular targets
Yulei Li, Ming‐Hao Wu, Yinxue Fu, et al.
Pharmacological Research (2024) Vol. 203, pp. 107137-107137
Open Access | Times Cited: 15
Prediction of lysine crotonylation sites by incorporating the composition of k -spaced amino acid pairs into Chou’s general PseAAC
Zhe Ju, Jianjun He
Journal of Molecular Graphics and Modelling (2017) Vol. 77, pp. 200-204
Closed Access | Times Cited: 87
Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC
Shengli Zhang, Xin Duan
Journal of Theoretical Biology (2017) Vol. 437, pp. 239-250
Closed Access | Times Cited: 87
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
Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers
Juan Mei, Ji Zhao
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 75
Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia
Lei Cai, Tao Huang, Jingjing Su, et al.
Molecular Therapy — Nucleic Acids (2018) Vol. 12, pp. 433-442
Open Access | Times Cited: 72
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: 70
UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components
Xiaoqiang Cui, Zhaomin Yu, Bin Yu, et al.
Chemometrics and Intelligent Laboratory Systems (2018) Vol. 184, pp. 28-43
Open Access | Times Cited: 67
Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams
Abdollah Dehzangi, Yosvany López, Sunil Pranit Lal, et al.
PLoS ONE (2018) Vol. 13, Iss. 2, pp. e0191900-e0191900
Open Access | Times Cited: 66
Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods
Xiuyun Zhai, Mingtong Chen, Wencong Lu
Computational Materials Science (2018) Vol. 151, pp. 41-48
Closed Access | Times Cited: 66
iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families
Muhammad Kabir, Saeed Ahmad, Muhammad Iqbal, et al.
Genomics (2019) Vol. 112, Iss. 1, pp. 276-285
Open Access | Times Cited: 66
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: 65
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
iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components
Lichao Zhang, Liang Kong
Journal of Theoretical Biology (2018) Vol. 441, pp. 1-8
Closed Access | Times Cited: 62
Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC
Ahmad Hassan Butt, Nouman Rasool, Yaser Daanial Khan
Molecular Biology Reports (2018) Vol. 45, Iss. 6, pp. 2295-2306
Closed Access | Times Cited: 62
iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou’s 5-step rule
Sharaf J. Malebary, Muhammad Safi ur Rehman, Yaser Daanial Khan
PLoS ONE (2019) Vol. 14, Iss. 11, pp. e0223993-e0223993
Open Access | Times Cited: 62

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