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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:

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

Showing 1-25 of 34 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
Prediction of bio-sequence modifications and the associations with diseases
Chunyan Ao, Liang Yu, Quan Zou
Briefings in Functional Genomics (2020) Vol. 20, Iss. 1, pp. 1-18
Closed Access | Times Cited: 72
SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties
Ghazaleh Taherzadeh, Abdollah Dehzangi, Maryam Golchin, et al.
Bioinformatics (2019) Vol. 35, Iss. 20, pp. 4140-4146
Closed Access | Times Cited: 61
Research progress in protein posttranslational modification site prediction
Wenying He, Leyi Wei, Quan Zou
Briefings in Functional Genomics (2018) Vol. 18, Iss. 4, pp. 220-229
Closed Access | Times Cited: 56
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
Systematic qualitative proteome-wide analysis of lysine malonylation profiling in Platycodon grandiflorus
Qingshan Yang, Shaowei Xu, Weimin Jiang, et al.
Amino Acids (2025) Vol. 57, Iss. 1
Open Access
PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact
Fuyi Li, Cunshuo Fan, Tatiana T. Marquez‐Lago, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 3, pp. 1069-1079
Open Access | Times Cited: 40
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
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
iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice
Hao Lv, Yang Zhang, Jia-Shu Wang, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 23
Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction
Subash C. Pakhrin, Suresh Pokharel, Hiroto Saigo, et al.
Methods in molecular biology (2022), pp. 285-322
Closed Access | Times Cited: 15
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
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
SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure
Abdollah Dehzangi, Yosvany López, Ghazaleh Taherzadeh, et al.
Molecules (2018) Vol. 23, Iss. 12, pp. 3260-3260
Open Access | Times Cited: 16
mUSP: a high-accuracy map of thein situcrosstalk of ubiquitylation and SUMOylation proteome predicted via the feature enhancement approach
Haodong Xu, Ru‐Ping Liang, You‐Gan Wang, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 11
Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration
Xin Liu, Liang Wang, Jian Li, et al.
BMC Genomics (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 11
Malonate utilization by Pseudomonas aeruginosa affects quorum‐sensing and virulence and leads to formation of mineralized biofilm‐like structures
Moamen M. Elmassry, Karishma Bisht, Jane A. Colmer‐Hamood, et al.
Molecular Microbiology (2021) Vol. 116, Iss. 2, pp. 516-537
Open Access | Times Cited: 10
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
Global Identification and Systematic Analysis of Lysine Malonylation in Maize (Zea mays L.)
Min Xu, Xiaomin Tian, Tingting Ku, et al.
Frontiers in Plant Science (2021) Vol. 12
Open Access | Times Cited: 8
iLMS, Computational Identification of Lysine-Malonylation Sites by Combining Multiple Sequence Features
Md Mehedi Hasan, Hiroyuki Kurata
(2018), pp. 356-359
Closed Access | Times Cited: 8
Enhanced Prediction of Lysine Propionylation Sites using Bi-peptide Evolutionary Features Resolving Data Imbalance
Md. Wakil Ahmad, Md. Easin Arafat, S.M. Shovan, et al.
2017 IEEE Region 10 Symposium (TENSYMP) (2020), pp. 1668-1671
Closed Access | Times Cited: 5
Protein Succinylation and Malonylation as Potential Biomarkers in Schizophrenia
Bradley J. Smith, Caroline Brandão‐Teles, Giuliana S. Zuccoli, et al.
Journal of Personalized Medicine (2022) Vol. 12, Iss. 9, pp. 1408-1408
Open Access | Times Cited: 3
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