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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:

TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides
Wanyun Zhou, Yufei Liu, Yingxin Li, et al.
Patterns (2023) Vol. 4, Iss. 3, pp. 100702-100702
Open Access | Times Cited: 21

Showing 21 citing articles:

mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations
Vinoth Kumar Sangaraju, Nhat Truong Pham, Leyi Wei, et al.
Journal of Molecular Biology (2024) Vol. 436, Iss. 17, pp. 168687-168687
Closed Access | Times Cited: 12
deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities
Jun Zhao, Hangcheng Liu, Liang‐I Kang, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access | Times Cited: 1
Dynamic Visualization of Computer-Aided Peptide Design for Cancer Therapeutics
Dan Hou, Haobin Zhou, Yuting Tang, et al.
Drug Design Development and Therapy (2025) Vol. Volume 19, pp. 1043-1065
Open Access | Times Cited: 1
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 | Times Cited: 1
PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model
Jiyun Han, Tongxin Kong, Juntao Liu
Communications Biology (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 8
deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model
Fei Zhao, Junhui Qiu, Dongyou Xiang, et al.
PeerJ (2024) Vol. 12, pp. e17729-e17729
Open Access | Times Cited: 6
CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder
Hina Ghafoor, Muhammad Nabeel Asim, Muhammad Ali Ibrahim, et al.
Computers in Biology and Medicine (2024) Vol. 176, pp. 108538-108538
Closed Access | Times Cited: 5
PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation
Muhammad Arif, Saleh Musleh, Huma Fida, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Leveraging protein language models for robust antimicrobial peptide detection
Lichao Zhang, Shuwen Xiong, Lei Xu, et al.
Methods (2025)
Closed Access
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
TaiChiNet: PCA-based Ying-Yang dilution of inter- and intra-BERT layers to represent anti-coronavirus peptides
Kongying Li, Shiying Ding, Zhe Guo, et al.
Expert Systems with Applications (2025), pp. 127786-127786
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
Cationic antimicrobial peptides: potential templates for anticancer agents
Yahson Fernando Varela-Quitián, Fabio Enrique Mendez-Rivera, David Andrés Bernal-Estévez
Frontiers in Medicine (2025) Vol. 12
Open Access
Comprehensive Assessment of BERT-Based Methods for Predicting Antimicrobial Peptides
W J Gao, Jun Zhao, Jianfeng Gui, et al.
Journal of Chemical Information and Modeling (2024)
Closed Access | Times Cited: 2
Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides
Yiting Deng, Shuhan Ma, Jiayu Li, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 13, pp. 10854-10854
Open Access | Times Cited: 5
An Augmented Sample Selection Framework for Prediction of Anticancer Peptides
Huawei Tao, Shuai Shan, Hongliang Fu, et al.
Molecules (2023) Vol. 28, Iss. 18, pp. 6680-6680
Open Access | Times Cited: 4
TriStack enables accurate identification of antimicrobial and anti-inflammatory peptides by combining machine learning and deep learning approaches
Jiyun Han, Qixuan Chen, J. T. Su, et al.
Future Generation Computer Systems (2024) Vol. 161, pp. 259-268
Closed Access | Times Cited: 1
Novel active Trp- and Arg-rich antimicrobial peptides with high solubility and low red blood cell toxicity designed using machine learning tools
Bridget A B Henson, Fucong Li, José Ausencio Álvarez-Huerta, et al.
International Journal of Antimicrobial Agents (2024), pp. 107399-107399
Open Access | Times Cited: 1
Discovery of anticancer peptides from natural and generated sequences using deep learning
Jianda Yue, Tingting Li, Jiawei Xu, et al.
International Journal of Biological Macromolecules (2024) Vol. 290, pp. 138880-138880
Closed Access | Times Cited: 1
AMP-EF: An Ensemble Framework of Extreme Gradient Boosting and Bidirectional Long Short-Term Memory Network for Identifying Antimicrobial Peptides
Shengli Zhang, Ya Zhao, Yunyun Liang
match Communications in Mathematical and in Computer Chemistry (2023) Vol. 91, Iss. 1, pp. 109-131
Open Access | Times Cited: 2
Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network
Jiyun Han, Shizhuo Zhang, Juntao Liu
STAR Protocols (2023) Vol. 4, Iss. 3, pp. 102541-102541
Open Access | Times Cited: 1
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