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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:

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides
Sajid Ahmed, Rafsanjani Muhammod, Zahid Khan, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 69

Showing 1-25 of 69 citing articles:

cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model
Shahid Akbar, Maqsood Hayat, Muhammad Tahir, et al.
Artificial Intelligence in Medicine (2022) Vol. 131, pp. 102349-102349
Closed Access | Times Cited: 95
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
Anti-Cancer Peptides: Status and Future Prospects
Gehane Ghaly, Hatem Tallima, Eslam Dabbish, et al.
Molecules (2023) Vol. 28, Iss. 3, pp. 1148-1148
Open Access | Times Cited: 46
Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides
Montserrat Goles, Anamaria Sanchez–Daza, Gabriel Cabas-Mora, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 30
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: 6
ACP-2DCNN: Deep learning-based model for improving prediction of anticancer peptides using two-dimensional convolutional neural network
Ali Ghulam, Farman Ali, Rahu Sikander, et al.
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 226, pp. 104589-104589
Closed Access | Times Cited: 52
H2Opred: a robust and efficient hybrid deep learning model for predicting 2’-O-methylation sites in human RNA
Nhat Truong Pham, Rajan Rakkiyapan, Jongsun Park, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 25
ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization
Mingyou Liu, Tao Wu, Xue Li, et al.
Frontiers in Genetics (2024) Vol. 15
Open Access | Times Cited: 12
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
ACP-ML: A sequence-based method for anticancer peptide prediction
Jilong Bian, Xuan Liu, Guanghui Dong, et al.
Computers in Biology and Medicine (2024) Vol. 170, pp. 108063-108063
Closed Access | Times Cited: 11
MA‐PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism
Liang Xiao, Haochen Zhao, Jianxin Wang
Protein Science (2024) Vol. 33, Iss. 4
Closed Access | Times Cited: 10
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: 9
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
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
Two-Sided Deep Reinforcement Learning for Dynamic Mobility-on-Demand Management with Mixed Autonomy
Jiaohong Xie, Yang Liu, Nan Chen
Transportation Science (2023) Vol. 57, Iss. 4, pp. 1019-1046
Closed Access | Times Cited: 21
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
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: 19
Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation
Lantian Yao, Wenshuo Li, Yuntian Zhang, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 5, pp. 4328-4328
Open Access | Times Cited: 18
ACP-ESM: A novel framework for classification of anticancer peptides using protein-oriented transformer approach
Zeynep Hilal Kilimci, Mustafa Yalçın
Artificial Intelligence in Medicine (2024) Vol. 156, pp. 102951-102951
Open Access | Times Cited: 8
Metaverse Applications in Bioinformatics: A Machine Learning Framework for the Discrimination of Anti-Cancer Peptides
Sufyan Danish, Asfandyar Khan, L. Minh Dang, et al.
Information (2024) Vol. 15, Iss. 1, pp. 48-48
Open Access | Times Cited: 6
ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies
Guolun Zhong, Lei Deng
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 1092-1104
Closed Access | Times Cited: 6
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
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: 14
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
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