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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 phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins
Salma Jamal, Waseem Ali, Priya Nagpal, et al.
Journal of Translational Medicine (2021) Vol. 19, Iss. 1
Open Access | Times Cited: 24

Showing 24 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: 17
Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network
Elham Khalili, Shahin Ramazi, Faezeh Ghanati, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Closed Access | Times Cited: 18
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
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
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
Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study
Mahdi Akbarzadeh, Nadia Alipour, Hamed Moheimani, et al.
Journal of Translational Medicine (2022) Vol. 20, Iss. 1
Open Access | Times Cited: 11
Comprehensive analysis of the lysine succinylome in fish oil-treated prostate cancer cells
Yifan Jiang, Chao He, Haokai Ye, et al.
Life Science Alliance (2023) Vol. 6, Iss. 11, pp. e202302131-e202302131
Open Access | Times Cited: 5
Comparative Phosphoproteomics of Neuro-2a Cells under Insulin Resistance Reveals New Molecular Signatures of Alzheimer’s Disease
Dayea Kim, Yeon Suk Jo, Han-Seul Jo, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 2, pp. 1006-1006
Open Access | Times Cited: 8
Holistic similarity-based prediction of phosphorylation sites for understudied kinases
Renfei Ma, Shangfu Li, Luca Parisi, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 4
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
PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models
Elly Poretsky, Carson M. Andorf, Taner Z. Sen
Plant Direct (2023) Vol. 7, Iss. 12
Open Access | Times Cited: 4
Phosphoproteomic analysis reveals the mechanisms of human umbilical cord mesenchymal stem cell-derived exosomes attenuate renal aging
Wenzhuo Yu, Jia Xu, Qiao Han, et al.
Journal of Proteomics (2024) Vol. 310, pp. 105335-105335
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
Accurately predicting microbial phosphorylation sites using evolutionary and structural features
Faisal Ahmed, Abdollah Dehzangi, Md Mehedi Hasan, et al.
Gene (2022) Vol. 851, pp. 146993-146993
Closed Access | Times Cited: 5
A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites
Shixian Wang, Lina Zhang, Runtao Yang, et al.
Biomolecules (2022) Vol. 12, Iss. 12, pp. 1854-1854
Open Access | Times Cited: 4
Identification of phosphorylation site using S-padding strategy based convolutional neural network
Yanjiao Zeng, Dongning Liu, Yang Wang
Health Information Science and Systems (2022) Vol. 10, Iss. 1
Open Access | Times Cited: 3
Machine learning-based exploration of enzyme-substrate networks: SET8-mediated methyllysine and its changing impact within cancer proteomes
Kyle K. Biggar, Nashira H Ridgeway, Anand Chopra, et al.
Research Square (Research Square) (2024)
Open Access
DF-Phos: Prediction of Protein phosphorylation Sites by Deep Forest
Zeynab Zahiri, Nasser Mehrshad, Maliheh Mehrshad
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
Shared and unique phosphoproteomics responses in skeletal muscle from exercise models and in hyperammonemic myotubes
Nicole Welch, Shashi Shekhar Singh, Ryan Musich, et al.
iScience (2022) Vol. 25, Iss. 11, pp. 105325-105325
Open Access | Times Cited: 1
DF-Phos: Prediction of Protein Phosphorylation Sites by Deep Forest
Zeynab Zahiri, Nasser Mehrshad, Maliheh Mehrshad
The Journal of Biochemistry (2023) Vol. 175, Iss. 4, pp. 447-456
Open Access
PhosphoLingo: protein language models for phosphorylation site prediction
Jasper Zuallaert, Pathmanaban Ramasamy, Robbin Bouwmeester, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access
XGboost-Ampy: Identification of AMPylation Protein Function Prediction Using Machine Learning
Zar Nawab Khan Swati, Ali Ghulam, Muhammad Sohail, et al.
VAWKUM Transactions on Computer Sciences (2022) Vol. 10, Iss. 2, pp. 83-95
Open Access
A Brief Review of Machine Learning Techniques for Protein Phosphorylation Sites Prediction.
Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, et al.
arXiv (Cornell University) (2021)
Closed Access
Translational signaling and systems biology
Marcello Maggiolini
Journal of Translational Medicine (2021) Vol. 19, Iss. 1
Open Access
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