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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:

ANNprob-ACPs: A novel anticancer peptide identifier based on probabilistic feature fusion approach
Tasmin Karim, Md. Shazzad Hossain Shaon, Md. Fahim Sultan, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107915-107915
Closed Access | Times Cited: 12

Showing 12 citing articles:

pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning
Muhammad Khalil Shahid, Maqsood Hayat, Wajdi Alghamdi, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models
Sadik Bhattarai, Hilal Tayara, Kil To Chong
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 13, pp. 4941-4957
Closed Access | Times Cited: 6
High-Accuracy Identification and Structure–Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry
Wanxing Li, Xuejing Liu, Yuanfa Liu, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Comprehensive Analysis of Computational Models for Prediction of Anticancer Peptides Using Machine Learning and Deep Learning
Farman Ali, Norazlin Ibrahim, Raed Alsini, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access
ACP-CLB: An Anticancer Peptide Prediction Model Based on Multichannel Discriminative Processing and Integration of Large Pretrained Protein Language Models
Aoyun Geng, Zhenjie Luo, Aohan Li, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Peptide classification landscape: An in-depth systematic literature review on peptide types, databases, datasets, predictors architectures and performance
Muhammad Nabeel Asim, Tayyaba Asif, Faiza Mehmood, et al.
Computers in Biology and Medicine (2025) Vol. 188, pp. 109821-109821
Closed Access
Bridging machine learning and peptide design for cancer treatment: a comprehensive review
Khosro Rezaee, Hossein Eslami
Artificial Intelligence Review (2025) Vol. 58, Iss. 5
Open Access
ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network
Lantian Yao, Peilin Xie, Jiahui Guan, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 5
Open Access | Times Cited: 4
Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations
Hong-Hin Cheong, Weimin Zuo, Jiarui Chen, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 15, pp. 6174-6189
Closed Access | Times Cited: 2
iACP-DFSRA: identification of anticancer peptides based on a dual-channel fusion strategy of ResCNN and Attention
Xin Wang, Zimeng Zhang, Chang Liu
Journal of Molecular Biology (2024), pp. 168810-168810
Closed Access | Times Cited: 2
Umami-gcForest: Construction of a predictive model for umami peptides based on deep forest
Shuaiqi Ji, Junrui Wu, Feiyu An, et al.
Food Chemistry (2024) Vol. 464, pp. 141826-141826
Closed Access | Times Cited: 1
Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods
Chengyan Wu, Zhi‐Xue Xu, Nan Li, et al.
Computational Biology and Chemistry (2024) Vol. 113, pp. 108207-108207
Closed Access
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