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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 26-50 of 69 citing articles:

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
To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification
Majed Alsanea, Abdulsalam Dukyil, Afnan Afnan, et al.
Sensors (2022) Vol. 22, Iss. 11, pp. 4005-4005
Open Access | Times Cited: 19
Deep-Representation-Learning-Based Classification Strategy for Anticancer Peptides
Shujaat Khan
Mathematics (2024) Vol. 12, Iss. 9, pp. 1330-1330
Open Access | Times Cited: 4
Contrastive learning for enhancing feature extraction in anticancer peptides
Byungjo Lee, Dongkwan Shin
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 4
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
An omics-driven computational model for angiogenic protein prediction: Advancing therapeutic strategies with Ens-deep-AGP
Naif Almusallam, Farman Ali, Atef Masmoudi, et al.
International Journal of Biological Macromolecules (2024), pp. 136475-136475
Closed Access | Times Cited: 4
Deep Learning for Antimicrobial Peptides: Computational Models and Databases
Xiangrun Zhou, Guixia Liu, Shuyuan Cao, et al.
Journal of Chemical Information and Modeling (2025)
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
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
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
pACPs-DNN: Predicting Anticancer peptides using Novel Peptide Transformation into Evolutionary and Structure Matrix-based Images with Self-attention Deep Learning Model
Muhammad Khalil Shahid, Maqsood Hayat, Ali Raza, et al.
Computational Biology and Chemistry (2025), pp. 108441-108441
Closed Access
GAN-ML: Advancing anticancer peptide prediction through innovative Deep Convolution Generative Adversarial Network data augmentation technique
Sadik Bhattarai, Kil To Chong, Hilal Tayara
Chemometrics and Intelligent Laboratory Systems (2025), pp. 105390-105390
Closed Access
ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction
Bingqing Han, Nan Zhao, Chengshi Zeng, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 16
The dynamic landscape of peptide activity prediction
Oriol Bárcenas, Carlos Pintado‐Grima, Katarzyna Sidorczuk, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 6526-6533
Open Access | Times Cited: 15
Efficient Mining of Anticancer Peptides from Gut Metagenome
Yue Ma, Xiaolin Liu, Xuan Zhang, et al.
Advanced Science (2023) Vol. 10, Iss. 25
Open Access | Times Cited: 8
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
Anticancer Peptides Classification Using Kernel Sparse Representation Classifier
Ehtisham Fazal, Muhammad Sohail Ibrahim, Seongyong Park, et al.
IEEE Access (2023) Vol. 11, pp. 17626-17637
Open Access | Times Cited: 7
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
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
Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides
Sayed Mehedi Azim, Aravind Balasubramanyam, Sheikh Rabiul Islam, et al.
Algorithms (2024) Vol. 17, Iss. 9, pp. 409-409
Open 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
Multi-headed Ensemble Residual CNN: A Powerful Tool for Fibroblast Growth Factor Prediction
Naif Almusallam, Farman Ali, Harish Kumar, et al.
Results in Engineering (2024) Vol. 24, pp. 103348-103348
Open Access | Times Cited: 2
iACP-GE: accurate identification of anticancer peptides by using gradient boosting decision tree and extra tree
Yunyun Liang, Xiaoqiu Ma
SAR and QSAR in environmental research (2022) Vol. 34, Iss. 1, pp. 1-19
Closed Access | Times Cited: 11
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: 10
SME-MFP: A novel spatiotemporal neural network with multiangle initialization embedding toward multifunctional peptides prediction
Jing Xu, Xiaoli Ruan, Jing Yang, et al.
Computational Biology and Chemistry (2024) Vol. 109, pp. 108033-108033
Closed Access | Times Cited: 1

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