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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:

iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset
Jianhua Jia, Zi Liu, Xuan Xiao, et al.
Analytical Biochemistry (2015) Vol. 497, pp. 48-56
Closed Access | Times Cited: 266

Showing 26-50 of 266 citing articles:

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins
Xuan Xiao, Xiang Cheng, Shengchao Su, et al.
Natural Science (2017) Vol. 09, Iss. 09, pp. 330-349
Open Access | Times Cited: 133
iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition
Xuan Xiao, Han-Xiao Ye, Zi Liu, et al.
Oncotarget (2016) Vol. 7, Iss. 23, pp. 34180-34189
Open Access | Times Cited: 127
Large-scale comparative assessment of computational predictors for lysine post-translational modification sites
Zhen Chen, Xuhan Liu, Fuyi Li, et al.
Briefings in Bioinformatics (2018) Vol. 20, Iss. 6, pp. 2267-2290
Open Access | Times Cited: 126
pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC
Xiang Cheng, Wei‐Zhong Lin, Xuan Xiao, et al.
Bioinformatics (2018) Vol. 35, Iss. 3, pp. 398-406
Open Access | Times Cited: 125
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
iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC
Bin Liu, Fan Weng, De-Shuang Huang, et al.
Bioinformatics (2018) Vol. 34, Iss. 18, pp. 3086-3093
Open Access | Times Cited: 121
Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods
Bin Liu, Hao Wu, Deyuan Zhang, et al.
Oncotarget (2017) Vol. 8, Iss. 8, pp. 13338-13343
Open Access | Times Cited: 120
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
iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition
Muhammad Arif, Maqsood Hayat, Zahoor Jan
Journal of Theoretical Biology (2018) Vol. 442, pp. 11-21
Closed Access | Times Cited: 108
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
Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC
Muslim Khan, Maqsood Hayat, Sher Afzal Khan, et al.
Journal of Theoretical Biology (2016) Vol. 415, pp. 13-19
Closed Access | Times Cited: 101
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
Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition
Zhe Ju, Shiyun Wang
Gene (2018) Vol. 664, pp. 78-83
Closed 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
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
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
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
pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Journal of Theoretical Biology (2018) Vol. 458, pp. 92-102
Closed Access | Times Cited: 84
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
A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule
Salman Khan, Mukhtaj Khan, Nadeem Iqbal, et al.
International Journal of Peptide Research and Therapeutics (2019) Vol. 26, Iss. 2, pp. 795-809
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

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