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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:

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: 35

Showing 26-50 of 35 citing articles:

An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides
Farwa Arshad, Saeed Ahmed, Aqsa Amjad, et al.
Analytical Biochemistry (2024) Vol. 691, pp. 115546-115546
Closed Access | Times Cited: 1
A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences
Jian He, Yanling Wu, Xuemei Pu, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 3, pp. 1741-1741
Open Access | Times Cited: 7
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
DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation
Faisal Ahmed, Alok Sharma, Swakkhar Shatabda, et al.
Proteins Structure Function and Bioinformatics (2024)
Closed Access
A Deep Model for Species-Specific Prediction of Ribonucleic-Acid-Binding Protein with Short Motifs
Zhisen Wei, Jun Rao, Yaojin Lin
Applied Sciences (2023) Vol. 13, Iss. 14, pp. 8231-8231
Open Access
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
Deep Learning Programming Using Python Case Study: Earthquake Prediction System
Basuki Rahmat, Budi Nugroho, Raka Adjie Kurniawan
Nusantara Science and Technology Proceedings (2021)
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
Recent advancements in predicting protein phosphorylation sites using machine learning methods
Adil Yousaf, Muhammad Rashid Rasheed, Muhammad Arif, et al.
2021 International Conference on Innovative Computing (ICIC) (2021), pp. 1-6
Closed Access
Comprehensive analysis of machine learning based predictors for identifying DNase I hypersensitive sites
Muhammad Rashid Rasheed, Mehwish Gill, Muhammad Asif Subhani, et al.
2021 International Conference on Innovative Computing (ICIC) (2021), pp. 1-6
Closed Access
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