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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:

AFP-SRC: identification of antifreeze proteins using sparse representation classifier
Muhammad Usman, Shujaat Khan, Seongyong Park, et al.
Neural Computing and Applications (2021) Vol. 34, Iss. 3, pp. 2275-2285
Closed Access | Times Cited: 12

Showing 12 citing articles:

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree
Adnan Khan, Jamal Uddin, Farman Ali, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 3, pp. 826-834
Closed Access | Times Cited: 26
Prediction of antifreeze proteins using machine learning
Adnan Khan, Jamal Uddin, Farman Ali, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 23
Comparative analysis of the existing methods for prediction of antifreeze proteins
Adnan Khan, Jamal Uddin, Farman Ali, et al.
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 232, pp. 104729-104729
Closed Access | Times Cited: 22
Deep-Representation-Learning-Based Classification Strategy for Anticancer Peptides
Shujaat Khan
Mathematics (2024) Vol. 12, Iss. 9, pp. 1330-1330
Open Access | Times Cited: 4
Anticancer Peptides Classification Using Kernel Sparse Representation Classifier
Ehtisham Fazal, Muhammad Sohail Ibrahim, Seongyong Park, et al.
IEEE Access (2023) Vol. 11, pp. 17626-17637
Open Access | Times Cited: 7
Prediction of anti-freezing proteins from their evolutionary profile
Nishant Kumar, Shubham Choudhury, Nisha Bajiya, et al.
(2024)
Closed Access | Times Cited: 2
Prediction and analysis of antifreeze proteins
Ryosuke Miyata, Yoshitaka Moriwaki, Tohru Terada, et al.
Heliyon (2021) Vol. 7, Iss. 9, pp. e07953-e07953
Open Access | Times Cited: 13
AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features
Muhammad Usman, Shujaat Khan, Seongyong Park, et al.
Current Issues in Molecular Biology (2021) Vol. 43, Iss. 3, pp. 1489-1501
Open Access | Times Cited: 13
Significance of Sequence Features in Classification of Protein–Protein Interactions Using Machine Learning
S. Sarath Raj, S. S. Vinod Chandra
The Protein Journal (2023) Vol. 43, Iss. 1, pp. 72-83
Closed Access | Times Cited: 5
VirusImmu: a novel ensemble machine learning approach for viral immunogenicity prediction
Jing Li, Zhongpeng Zhao, Chengzheng Tai, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 2
Prediction of Anti‐Freezing Proteins From Their Evolutionary Profile
Nishant Kumar, Shubham Choudhury, Nisha Bajiya, et al.
PROTEOMICS (2024)
Closed Access
Comprehensive Feature Extraction Model to Classify Interacting and Non-Interacting Proteins in Human Viruses using Random Forest Classifier
Sini S. Raj, S. S. Vinod Chandra
Research Square (Research Square) (2023)
Open Access
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