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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.

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Showing 1-25 of 52 citing articles:

A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites
Minjie Mou, Ziqi Pan, Zhimeng Zhou, et al.
Research (2023) Vol. 6
Open Access | Times Cited: 54
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review
Minhyeok Lee
Molecules (2023) Vol. 28, Iss. 13, pp. 5169-5169
Open Access | Times Cited: 37
Multi-omics integration method based on attention deep learning network for biomedical data classification
Ping Gong, Lei Cheng, Zhiyuan Zhang, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 231, pp. 107377-107377
Closed Access | Times Cited: 27
Mini-review: Recent advances in post-translational modification site prediction based on deep learning
Lingkuan Meng, Wai‐Sum Chan, Lei Huang, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 3522-3532
Open Access | Times Cited: 31
LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model
Subash C. Pakhrin, Suresh Pokharel, Pawel Pratyush, et al.
Journal of Proteome Research (2023) Vol. 22, Iss. 8, pp. 2548-2557
Closed Access | Times Cited: 20
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 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
FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics
Guangyu Zhang, Cai Zhang, Mingyue Cai, et al.
International Journal of Biological Macromolecules (2024) Vol. 266, pp. 131180-131180
Closed Access | Times Cited: 6
Protein Function Prediction Based on PPI Networks: Network Reconstruction vs Edge Enrichment
Jiaogen Zhou, Wei Xiong, Yang Wang, et al.
Frontiers in Genetics (2021) Vol. 12
Open Access | Times Cited: 39
Phosformer: An explainable Transformer model for protein kinase-specific phosphorylation predictions
Zhongliang Zhou, Wayland Yeung, Nathan Gravel, et al.
Bioinformatics (2023) Vol. 39, Iss. 2
Open Access | Times Cited: 14
PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures
Ziyuan Yu, Jialin Yu, Hongmei Wang, et al.
Analytical Biochemistry (2024) Vol. 690, pp. 115510-115510
Closed Access | Times Cited: 5
Insights on post-translational modifications in fatty liver and fibrosis progression
C. Nageswara Raju, Kavitha Sankaranarayanan
Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease (2025) Vol. 1871, Iss. 3, pp. 167659-167659
Closed Access
MMFuncPhos: A Multi‐Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types
Juan Xie, Ruihan Dong, Jintao Zhu, et al.
Advanced Science (2025)
Open Access
Deep Learning Techniques Applied to Phosphorylation Site Prediction: A Systematic Review
Antônio Carlos da Silva Júnior, André Massahiro Shimaoka, Luciano Rodrigo Lopes, et al.
SN Computer Science (2025) Vol. 6, Iss. 4
Closed Access
Protein phosphorylation database and prediction tools
Mingxiao Zhao, Qiang Chen, Fulai Li, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 12
SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition
Qiuwen Sun, Lei Cheng, Ao Meng, et al.
Frontiers in Genetics (2023) Vol. 13
Open Access | Times Cited: 10
Deciphering the functional landscape of phosphosites with deep neural network
Zhongjie Liang, Tonghai Liu, Qi Li, et al.
Cell Reports (2023) Vol. 42, Iss. 9, pp. 113048-113048
Open Access | Times Cited: 9
Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models
Fei Zhu, Sijie Yang, Fanwang Meng, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 14, pp. 3331-3345
Closed Access | Times Cited: 15
DL-PPI: a method on prediction of sequenced protein–protein interaction based on deep learning
Jiahui Wu, Bo Liu, Jidong Zhang, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 8
R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting
Hongyan Shi, Shengli Zhang, Xinjie Li
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Closed Access | Times Cited: 13
DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism
Haiwei Zhou, Wenxi Tan, Shaoping Shi
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 7
Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery
Neha Varshney, Abhinava K. Mishra
Proteomes (2023) Vol. 11, Iss. 2, pp. 16-16
Open Access | Times Cited: 7
MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction
Zhongyu Wang, Zhaohong Deng, Wei Zhang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Closed Access | Times Cited: 7
DL-SPhos: Prediction of serine phosphorylation sites using transformer language model
Palistha Shrestha, Jeevan Kandel, Hilal Tayara, et al.
Computers in Biology and Medicine (2024) Vol. 169, pp. 107925-107925
Closed Access | Times Cited: 2
Using explainable machine learning to uncover the kinase-substrate interaction landscape
Zhongliang Zhou, Wayland Yeung, Saber Soleymani, et al.
Bioinformatics (2024) Vol. 40, Iss. 2
Open Access | Times Cited: 2
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