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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:

DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion
Ruifen Cao, Meng Wang, Yannan Bin, et al.
PeerJ (2021) Vol. 9, pp. e11906-e11906
Open Access | Times Cited: 19

Showing 19 citing articles:

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding
Qitong Yuan, Keyi Chen, Yimin Yu, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 1
Closed Access | Times Cited: 74
Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning
Jielu Yan, Jianxiu Cai, Bob Zhang, et al.
Antibiotics (2022) Vol. 11, Iss. 10, pp. 1451-1451
Open Access | Times Cited: 71
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 | 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: 8
ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information
Mingwei Sun, Haoyuan Hu, Wei Pang, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 20, pp. 15447-15447
Open Access | Times Cited: 15
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
ACP-check: An anticancer peptide prediction model based on bidirectional long short-term memory and multi-features fusion strategy
Lun Zhu, Chenyang Ye, Xuemei Hu, et al.
Computers in Biology and Medicine (2022) Vol. 148, pp. 105868-105868
Closed Access | Times Cited: 20
iAMP-EmGCN: A new design for identifying antimicrobial peptides based on BERT and Graph Convolutional Network
Wenxuan Xing, Jie Zhang, Chen Li, et al.
Expert Systems with Applications (2025), pp. 127811-127811
Closed Access
ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier
Huijia Song, Xiaozhu Lin, Huainian Zhang, et al.
Computational Biology and Chemistry (2024) Vol. 110, pp. 108091-108091
Closed Access | Times Cited: 3
ACP-PDAFF: Pretrained model and dual-channel attentional feature fusion for anticancer peptides prediction
Xinyi Wang, Shunfang Wang
Computational Biology and Chemistry (2024) Vol. 112, pp. 108141-108141
Closed Access | Times Cited: 2
FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses
Ruifen Cao, Weiling Hu, Pi-Jing Wei, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 6
Closed Access | Times Cited: 6
Studying the effect of gene fusion of A and C types capsular synthesizing enzymes and anticancer sequence on inducing the expression of apoptotic BCL-2, BAX, and Caspase-3 genes by Real-time RT-PCR method
Elmira Babakanrad, Taher Mohammadian, Davoud Esmaeili, et al.
Heliyon (2023) Vol. 9, Iss. 5, pp. e16326-e16326
Open Access | Times Cited: 4
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 | Times Cited: 1
DPProm: A Two-Layer Predictor for Identifying Promoters and Their Types on Phage Genome Using Deep Learning
Chen Wang, Junyin Zhang, Li Cheng, et al.
IEEE Journal of Biomedical and Health Informatics (2022) Vol. 26, Iss. 10, pp. 5258-5266
Closed Access | Times Cited: 4
Chemical Strategies towards the Development of Effective Anticancer Peptides
Cuicui Li, Kang Jin
Current Medicinal Chemistry (2023) Vol. 31, Iss. 14, pp. 1839-1873
Closed Access | Times Cited: 2
PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences
You Li, Jianyi Lyu, Yao‐Qun Wu, et al.
Life (2022) Vol. 12, Iss. 2, pp. 307-307
Open Access | Times Cited: 3
Integrating multiple sequence features for identifying anticancer peptides
Hongliang Zou, Fan Yang, Zhijian Yin
Computational Biology and Chemistry (2022) Vol. 99, pp. 107711-107711
Closed Access | Times Cited: 3
A Surrogate-Assisted Genetic Algorithm Framework to Discover Peptides Against COVID-19 Virus
Elias A.D. Silva, Lucas Sousa Palmeira, Marcelo Augusto Garcia-Júnior, et al.
2022 IEEE Congress on Evolutionary Computation (CEC) (2024), pp. 1-8
Closed Access
The Effect of Recombinant Fusion Peptide Capsular Biosynthesizing Enzymes A, C Streptococcus agalactiae and Anti-Cancer Peptide against Cell Line Hela by Real-time RT- PCR and Flow cytometry
Elmira Babakanrad, Taher Mohammadian, Davoud Esmaeili, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
Breast and Lung Anticancer Peptides Classification Using N-Grams and Ensemble Learning Techniques
Ayad R. Abbas, Bashar Saadoon Mahdi, Osamah Y. Fadhil
Big Data and Cognitive Computing (2022) Vol. 6, Iss. 2, pp. 40-40
Open Access | Times Cited: 2
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