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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:

iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition
Yan Xu, Xin Wen, Xiaojian Shao, et al.
International Journal of Molecular Sciences (2014) Vol. 15, Iss. 5, pp. 7594-7610
Open Access | Times Cited: 200

Showing 26-50 of 200 citing articles:

iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition
Wang‐Ren Qiu, Shiyu Jiang, Zhaochun Xu, et al.
Oncotarget (2017) Vol. 8, Iss. 25, pp. 41178-41188
Open Access | Times Cited: 198
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
pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites
Xiang Cheng, Shuguang Zhao, Wei‐Zhong Lin, et al.
Bioinformatics (2017) Vol. 33, Iss. 22, pp. 3524-3531
Open Access | Times Cited: 183
iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC
Jianhua Jia, Zi Liu, Xuan Xiao, et al.
Oncotarget (2016) Vol. 7, Iss. 23, pp. 34558-34570
Open Access | Times Cited: 174
Protein acetylation sites with complex-valued polynomial model
Wenzheng Bao, Bin Yang
Frontiers of Computer Science (2024) Vol. 18, Iss. 3
Closed Access | Times Cited: 26
iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model
Wang‐Ren Qiu, Xuan Xiao, Wei‐Zhong Lin, et al.
Journal of Biomolecular Structure and Dynamics (2014) Vol. 33, Iss. 8, pp. 1731-1742
Open Access | Times Cited: 165
pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Genomics (2017) Vol. 110, Iss. 4, pp. 231-239
Open Access | Times Cited: 165
iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Oncotarget (2016) Vol. 7, Iss. 28, pp. 44310-44321
Open Access | Times Cited: 161
iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Genomics (2017) Vol. 110, Iss. 5, pp. 239-246
Closed Access | Times Cited: 160
iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets
Jianhua Jia, Zi Liu, Xuan Xiao, et al.
Molecules (2016) Vol. 21, Iss. 1, pp. 95-95
Open Access | Times Cited: 159
pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Gene (2017) Vol. 628, pp. 315-321
Closed Access | Times Cited: 159
pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Bioinformatics (2017) Vol. 34, Iss. 9, pp. 1448-1456
Open Access | Times Cited: 159
Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy
Bin Liu, Longyun Fang, Shanyi Wang, et al.
Journal of Theoretical Biology (2015) Vol. 385, pp. 153-159
Closed Access | Times Cited: 156
Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model
Zaheer Ullah Khan, Maqsood Hayat, Muazzam A. Khan
Journal of Theoretical Biology (2014) Vol. 365, pp. 197-203
Closed Access | Times Cited: 154
iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space
Shahid Akbar, Maqsood Hayat, Muhammad Iqbal, et al.
Artificial Intelligence in Medicine (2017) Vol. 79, pp. 62-70
Closed Access | Times Cited: 152
iPhos-PseEn: Identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier
Wang‐Ren Qiu, Xuan Xiao, Zhaochun Xu, et al.
Oncotarget (2016) Vol. 7, Iss. 32, pp. 51270-51283
Open Access | Times Cited: 149
Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition
Jianhua Jia, Zi Liu, Xuan Xiao, et al.
Journal of Biomolecular Structure and Dynamics (2015) Vol. 34, Iss. 9, pp. 1946-1961
Open Access | Times Cited: 140
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
Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine
Ravindra Kumar, Abhishikha Srivastava, Bandana Kumari, et al.
Journal of Theoretical Biology (2014) Vol. 365, pp. 96-103
Closed Access | Times Cited: 133
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
iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC
Yaser Daanial Khan, Nouman Rasool, Waqar Hussain, et al.
Analytical Biochemistry (2018) Vol. 550, pp. 109-116
Closed Access | Times Cited: 128
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
O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique
Cangzhi Jia, Yun Zuo, Quan Zou
Bioinformatics (2018) Vol. 34, Iss. 12, pp. 2029-2036
Open Access | Times Cited: 127
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

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