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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:

Prediction of Lysine Malonylation Sites Based on Pseudo Amino Acid
Qilin Xiang, Kai‐Yan Feng, Bo Liao, et al.
Combinatorial Chemistry & High Throughput Screening (2017) Vol. 20, Iss. 7
Closed Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework
Yanju Zhang, Ruopeng Xie, Jiawei Wang, et al.
Briefings in Bioinformatics (2018) Vol. 20, Iss. 6, pp. 2185-2199
Open Access | Times Cited: 98
A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction
Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, et al.
Genomics Proteomics & Bioinformatics (2023) Vol. 21, Iss. 6, pp. 1266-1285
Open Access | Times Cited: 17
Malsite-Deep: Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy
Minghui Wang, Lili Song, Yaqun Zhang, et al.
Knowledge-Based Systems (2022) Vol. 240, pp. 108191-108191
Closed Access | Times Cited: 22
Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences
Shahin Ramazi, Seyed Amir Hossein Tabatabaei, Elham Khalili, et al.
Database (2024) Vol. 2024
Open Access | Times Cited: 5
LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites
Guohua Huang, Qingfeng Shen, Guiyang Zhang, et al.
BioMed Research International (2021) Vol. 2021, pp. 1-10
Open Access | Times Cited: 28
Global profiling of protein lysine malonylation in mouse cardiac hypertrophy
Lifei Wu, Deping Wang, Jing Shen, et al.
Journal of Proteomics (2022) Vol. 266, pp. 104667-104667
Closed Access | Times Cited: 19
PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 36
Predicting lysine‐malonylation sites of proteins using sequence and predicted structural features
Ghazaleh Taherzadeh, Yuedong Yang, Haodong Xu, et al.
Journal of Computational Chemistry (2018) Vol. 39, Iss. 22, pp. 1757-1763
Closed Access | Times Cited: 34
DeepMal: Accurate prediction of protein malonylation sites by deep neural networks
Minghui Wang, Xiaoqiang Cui, Shan Li, et al.
Chemometrics and Intelligent Laboratory Systems (2020) Vol. 207, pp. 104175-104175
Closed Access | Times Cited: 31
DE-MHAIPs: Identification of SARS-CoV-2 phosphorylation sites based on differential evolution multi-feature learning and multi-head attention mechanism
Minghui Wang, Yan Lu, Jihua Jia, et al.
Computers in Biology and Medicine (2023) Vol. 160, pp. 106935-106935
Open Access | Times Cited: 10
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
EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
BMC Genomics (2019) Vol. 19, Iss. S9
Open Access | Times Cited: 24
RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites
Hussam J. AL-barakati, Niraj Thapa, Hiroto Saigo, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 852-860
Open Access | Times Cited: 21
Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features
Md. Wakil Ahmad, Md. Easin Arafat, Ghazaleh Taherzadeh, et al.
IEEE Access (2020) Vol. 8, pp. 77888-77902
Open Access | Times Cited: 19
Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
BMC Molecular and Cell Biology (2019) Vol. 20, Iss. S2
Open Access | Times Cited: 19
SEMal: Accurate protein malonylation site predictor using structural and evolutionary information
Shubhashis Roy Dipta, Ghazaleh Taherzadeh, Md. Wakil Ahmad, et al.
Computers in Biology and Medicine (2020) Vol. 125, pp. 104022-104022
Closed Access | Times Cited: 17
Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins
Chia‐Ru Chung, Yaping Chang, Yu-Lin Hsu, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 16
Prediction of protein ubiquitination sites via multi-view features based on eXtreme gradient boosting classifier
Yushuang Liu, Shuping Jin, Lili Song, et al.
Journal of Molecular Graphics and Modelling (2021) Vol. 107, pp. 107962-107962
Closed Access | Times Cited: 13
Machine learning-based approaches for ubiquitination site prediction in human proteins
Mahdi Pourmirzaei, Shahin Ramazi, Farzaneh Esmaili, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 4
Res-GCN: identification of protein phosphorylation sites using graph convolutional network and residual network
Minghui Wang, Jihua Jia, Fei Xu, et al.
Computational Biology and Chemistry (2024) Vol. 112, pp. 108183-108183
Closed Access | Times Cited: 1
A hybrid feature extraction scheme for efficient malonylation site prediction
Ali Ghanbari Sorkhi, Jamshid Pirgazi, Vahid Ghasemi
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 7
RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
Genes (2020) Vol. 11, Iss. 12, pp. 1524-1524
Open Access | Times Cited: 9
A Transfer Learning-Based Approach for Lysine Propionylation Prediction
Ang Li, Yingwei Deng, Tan Yan, et al.
Frontiers in Physiology (2021) Vol. 12
Open Access | Times Cited: 5
PreCar_Deep：A deep learning framework for prediction of protein carbonylation sites based on Borderline-SMOTE strategy
Lili Song, Yaokui Xu, Minghui Wang, et al.
Chemometrics and Intelligent Laboratory Systems (2021) Vol. 218, pp. 104428-104428
Closed Access | Times Cited: 5
PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids
Abel Chandra, Alok Sharma
Research Square (Research Square) (2019)
Open Access | Times Cited: 4
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