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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:

AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction
Jingjing Liu, Minghao Li, Xin Chen
Methods (2022) Vol. 207, pp. 38-43
Closed Access | Times Cited: 9

Showing 9 citing articles:

iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model
Wenxuan Xing, Jie Zhang, Chen Li, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 22
Biological Activity of Natural and Synthetic Peptides as Anticancer Agents
Luana Vittoria Bauso, Valeria La Fauci, S. Munaò, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 13, pp. 7264-7264
Open Access | Times Cited: 9
CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only
Xuetong Yang, Junru Jin, Ruheng Wang, et al.
Journal of Chemical Information and Modeling (2023) Vol. 64, Iss. 7, pp. 2807-2816
Closed Access | Times Cited: 18
An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM
Onur Karakaya, Zeynep Hilal Kilimci
PeerJ Computer Science (2024) Vol. 10, pp. e1831-e1831
Open Access | Times Cited: 7
Artificial intelligence for the discovery of antimicrobial peptides
Paola Ruiz Puentes, Nicolás Aparicio, Pablo Arbeláez
Elsevier eBooks (2025), pp. 59-79
Closed Access
Selenium Decipher: Trapping of Native Selenomethionine-Containing Peptides in Selenium-Enriched Milk and Unveiling the Deterioration after Ultrahigh-Temperature Treatment
Xixuan Wu, Wei Jia
Analytical Chemistry (2024) Vol. 96, Iss. 3, pp. 1156-1166
Closed Access | Times Cited: 4
Topology-Enhanced Machine Learning Model (Top-ML) for Anticancer Peptide Prediction
Joshua Zhi En Tan, JunJie Wee, Xue Gong, et al.
Journal of Chemical Information and Modeling (2025)
Open Access
New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
Sayeda Muntaha Ferdous, Shafayat Bin Shabbir Mugdha, Abdollah Dehzangi
Algorithms (2024) Vol. 17, Iss. 6, pp. 247-247
Open Access | Times Cited: 2
Editorial: Artificial intelligence in drug discovery and development
Leyi Wei, Quan Zou, Xiangxiang Zeng
Methods (2024) Vol. 226, pp. 133-137
Closed Access
Classifying Anticancer Peptides Using Gated Recurrent Unit
Onur Karakaya, Zeynep Hilal Kilimci
2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (2023), pp. 1-6
Closed Access
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