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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:

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

Showing 1-25 of 92 citing articles:

LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
Cheng Chen, Qingmei Zhang, Qin Ma, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 191, pp. 54-64
Open Access | Times Cited: 230
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
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: 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
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
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
iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC
Yaser Daanial Khan, Nouman Rasool, Waqar Hussain, et al.
Molecular Biology Reports (2018) Vol. 45, Iss. 6, pp. 2501-2509
Closed Access | Times Cited: 67
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
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
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
SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data
Nguyen Quoc Khanh Le, Van-Nui Nguyen
PeerJ Computer Science (2019) Vol. 5, pp. e177-e177
Open Access | Times Cited: 64
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
Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC
Ahmad Hassan Butt, Nouman Rasool, Yaser Daanial Khan
Journal of Theoretical Biology (2019) Vol. 473, pp. 1-8
Closed Access | Times Cited: 60
iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5- Step Rule and General Pseudo Amino Acid Composition
Asma Ehsan, Muhammad Khalid Mahmood, Yaser Daanial Khan, et al.
Current Genomics (2019) Vol. 20, Iss. 2, pp. 124-133
Open Access | Times Cited: 56
Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC
Saba Amanat, Adeel Ashraf, Waqar Hussain, et al.
Current Bioinformatics (2019) Vol. 15, Iss. 5, pp. 396-407
Closed Access | Times Cited: 54
iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou’s PseAAC
Yaser Daanial Khan, Nabeel Sabir Khan, Sheraz Naseer, et al.
PeerJ (2021) Vol. 9, pp. e11581-e11581
Open Access | Times Cited: 46
Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach
Baoguang Tian, Xue Wu, Cheng Chen, et al.
Journal of Theoretical Biology (2018) Vol. 462, pp. 329-346
Open Access | Times Cited: 56
MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components
Ahmad Jamal, Maqsood Hayat
Journal of Theoretical Biology (2018) Vol. 463, pp. 99-109
Closed Access | Times Cited: 56
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