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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:

iGlu-Lys: A Predictor for Lysine Glutarylation Through Amino Acid Pair Order Features
Yan Xu, Yingxi Yang, Jun Ding, et al.
IEEE Transactions on NanoBioscience (2018) Vol. 17, Iss. 4, pp. 394-401
Closed Access | Times Cited: 25

Showing 25 citing articles:

Insights into the post-translational modification and its emerging role in shaping the tumor microenvironment
Wen Li, Feifei Li, Xia Zhang, et al.
Signal Transduction and Targeted Therapy (2021) Vol. 6, Iss. 1
Open Access | Times Cited: 122
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
iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions
Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, et al.
Journal of Biomolecular Structure and Dynamics (2021) Vol. 40, Iss. 22, pp. 11691-11704
Closed Access | Times Cited: 51
Deepro-Glu: combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sites
Xiao Wang, Zhaoyuan Ding, Rong Wang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 18
A Novel Method for Identification of Glutarylation Sites Combining Borderline-SMOTE With Tomek Links Technique in Imbalanced Data
Ning Qiao, Xiaowei Zhao, Zhiqiang Ma
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 19, Iss. 5, pp. 2632-2641
Closed Access | Times Cited: 39
Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou’s General PseAAC
Lidong Wang, Ruijun Zhang, Yashuang Mu
Journal of Theoretical Biology (2018) Vol. 461, pp. 51-58
Closed Access | Times Cited: 45
RF-GlutarySite: a random forest based predictor for glutarylation sites
Hussam J. AL-barakati, Hiroto Saigo, Robert H. Newman, et al.
Molecular Omics (2019) Vol. 15, Iss. 3, pp. 189-204
Closed Access | Times Cited: 36
iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier
Lijun Dou, Xiaoling Li, Lichao Zhang, et al.
Journal of Proteome Research (2020) Vol. 20, Iss. 1, pp. 191-201
Closed Access | Times Cited: 31
The first knock-in rat model for glutaric aciduria type I allows further insights into pathophysiology in brain and periphery
Mary Gonzalez Melo, Noémie Remacle, Hong-Phuc Cudré-Cung, et al.
Molecular Genetics and Metabolism (2021) Vol. 133, Iss. 2, pp. 157-181
Open Access | Times Cited: 26
Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features
Md. Easin Arafat, Md. Wakil Ahmad, S.M. Shovan, et al.
Genes (2020) Vol. 11, Iss. 9, pp. 1023-1023
Open Access | Times Cited: 24
Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction
Chuan-Ming Liu, Van-Dai Ta, Nguyen Quoc Khanh Le, et al.
Life (2022) Vol. 12, Iss. 8, pp. 1213-1213
Open Access | Times Cited: 14
Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme
Smita Kumari, Rohan Gupta, Rashmi K. Ambasta, et al.
Biochimica et Biophysica Acta (BBA) - Reviews on Cancer (2023) Vol. 1878, Iss. 6, pp. 188999-188999
Closed Access | Times Cited: 7
Functions and Mechanisms of Lysine Glutarylation in Eukaryotes
Longxiang Xie, Yafei Xiao, Fucheng Meng, et al.
Frontiers in Cell and Developmental Biology (2021) Vol. 9
Open Access | Times Cited: 16
ProtTrans-Glutar: Incorporating Features From Pre-trained Transformer-Based Models for Predicting Glutarylation Sites
Fatma Indriani, Kunti Mahmudah, Bedy Purnama, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 12
DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet
Jianhua Jia, Mingwei Sun, Genqiang Wu, et al.
Mathematical Biosciences & Engineering (2022) Vol. 20, Iss. 2, pp. 2815-2830
Open Access | Times Cited: 12
AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network
Muhammad Usman, Jeong A Lee
(2019)
Open Access | Times Cited: 19
Computational Identification of Lysine Glutarylation Sites Using Positive- Unlabeled Learning
Zhe Ju, Shiyun Wang
Current Genomics (2020) Vol. 21, Iss. 3, pp. 204-211
Open Access | Times Cited: 14
SSKM_Succ: A Novel Succinylation Sites Prediction Method Incorporating K-Means Clustering With a New Semi-Supervised Learning Algorithm
Ning Qiao, Zhiqiang Ma, Xiaowei Zhao, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020) Vol. 19, Iss. 1, pp. 643-652
Closed Access | Times Cited: 12
GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier
Xin Liu, Bao Zhu, Xia-Wei Dai, et al.
BMC Genomics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 2
iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network
Ang Sun, Xuan Xiao, Zhaochun Xu
Computational and Mathematical Methods in Medicine (2021) Vol. 2021, pp. 1-9
Open Access | Times Cited: 5
AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network
Muhammad Usman, Jeong A Lee
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 3
Predicting Lysine Glutarylation Sites by Combining Multiple Feature Selection Methods
Tasnima Yeasmin, Md. Al Mehedi Hasan, Tasfin Jayed
(2019), pp. 252-255
Closed Access | Times Cited: 2
DeepGlut: A Deep Learning Framework for Prediction of Glutarylation Sites in Proteins
Urmi Sen, Md. Al Mehedi Hasan
2017 IEEE Region 10 Symposium (TENSYMP) (2020), pp. 941-944
Closed Access | Times Cited: 2
GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on Feature Fusion and GBDT classifier
Xin Liu, Bao Zhu, Xia-Wei Dai, et al.
Research Square (Research Square) (2023)
Open Access
Additional file 1 of GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier
Xin Liu, Bao Zhu, Xia-Wei Dai, et al.
Figshare (2023)
Open Access
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