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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:

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites
Zhen Chen, Xuhan Liu, Fuyi Li, et al.
Briefings in Bioinformatics (2018) Vol. 20, Iss. 6, pp. 2267-2290
Open Access | Times Cited: 126

Showing 26-50 of 126 citing articles:

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: 18
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
Prediction of S-nitrosylation sites by integrating support vector machines and random forest
Md Mehedi Hasan, Balachandran Manavalan, Mst. Shamima Khatun, et al.
Molecular Omics (2019) Vol. 15, Iss. 6, pp. 451-458
Closed Access | Times Cited: 51
PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins
Yanju Zhang, Sha Yu, Ruopeng Xie, et al.
Bioinformatics (2019) Vol. 36, Iss. 3, pp. 704-712
Closed Access | Times Cited: 48
Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites
Md Mehedi Hasan, Mst. Shamima Khatun, Hiroyuki Kurata
Cells (2019) Vol. 8, Iss. 2, pp. 95-95
Open Access | Times Cited: 46
nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning
Yongzi Chen, Zhuozhi Wang, Yanan Wang, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 39
GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites
Chenwei Wang, Xiaodan Tan, Dachao Tang, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 2
Closed Access | Times Cited: 33
RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences
Lei Chen, Yuwei Chen
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 14
csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou’s 5-step rule
Ze Liu, Wei Dong, Wei Jiang, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 42
HybridSucc: A Hybrid-Learning Architecture for General and Species-Specific Succinylation Site Prediction
Wanshan Ning, Haodong Xu, Peiran Jiang, et al.
Genomics Proteomics & Bioinformatics (2020) Vol. 18, Iss. 2, pp. 194-207
Open Access | Times Cited: 39
A feature-based approach to predict hot spots in protein–DNA binding interfaces
Sijia Zhang, Le Zhao, Chun-Hou Zheng, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 3, pp. 1038-1046
Closed Access | Times Cited: 38
DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information
Saeed Ahmed, Muhammad Kabir, Muhammad Arif, et al.
Analytical Biochemistry (2020) Vol. 612, pp. 113955-113955
Closed Access | Times Cited: 36
SulSite-GTB: identification of protein S-sulfenylation sites by fusing multiple feature information and gradient tree boosting
Minghui Wang, Xiaoqiang Cui, Bin Yu, et al.
Neural Computing and Applications (2020) Vol. 32, Iss. 17, pp. 13843-13862
Closed Access | Times Cited: 33
Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks
Yiming Zhao, Ningning He, Zhen Chen, et al.
IEEE Access (2020) Vol. 8, pp. 14244-14252
Open Access | Times Cited: 32
PredCID: prediction of driver frameshift indels in human cancer
Zhenyu Yue, Xinlu Chu, Junfeng Xia
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 30
Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana
Md. Parvez Mosharaf, Md. Mehedi Hassan, Fee Faysal Ahmed, et al.
Computational Biology and Chemistry (2020) Vol. 85, pp. 107238-107238
Closed Access | Times Cited: 28
HSM6AP: a high-precision predictor for the Homo sapiens N6-methyladenosine (m^6 A) based on multiple weights and feature stitching
Jing Li, He Shida, Fei Guo, et al.
RNA Biology (2021) Vol. 18, Iss. 11, pp. 1882-1892
Open Access | Times Cited: 26
Computational approaches to predict protein functional families and functional sites
Clemens Rauer, Neeladri Sen, Vaishali Waman, et al.
Current Opinion in Structural Biology (2021) Vol. 70, pp. 108-122
Open Access | Times Cited: 25
Dynamic In Vivo Mapping of the Methylproteome Using a Chemoenzymatic Approach
Jonathan Farhi, Benjamin Emenike, Richard S. Lee, et al.
Journal of the American Chemical Society (2025) Vol. 147, Iss. 9, pp. 7214-7230
Open Access
MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human
Yuhua Zuo, Xingze Fang, Jiankang Chen, et al.
Briefings in Bioinformatics (2025) Vol. 26, Iss. 2
Open Access
Decoding RIG-I Ubiquitination in Fish EPC Cells: Site Identification and Antiviral Implications
Feihong Liu, Zheru Ma, Jieming Lu, et al.
Fish & Shellfish Immunology (2025), pp. 110393-110393
Closed Access
Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses
Kuo‐Chen Chou
International Journal of Peptide Research and Therapeutics (2019) Vol. 26, Iss. 2, pp. 1085-1098
Closed Access | Times Cited: 29
Formator: Predicting Lysine Formylation Sites Based on the Most Distant Undersampling and Safe-Level Synthetic Minority Oversampling
Cangzhi Jia, Meng Zhang, Cunshuo Fan, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019) Vol. 18, Iss. 5, pp. 1937-1945
Closed Access | Times Cited: 26
DeepTL-Ubi: A novel deep transfer learning method for effectively predicting ubiquitination sites of multiple species
Yu Liu, Ao Li, Xing‐Ming Zhao, et al.
Methods (2020) Vol. 192, pp. 103-111
Closed Access | Times Cited: 24
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

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