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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:

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space
Shahid Akbar, Maqsood Hayat, Muhammad Iqbal, et al.
Artificial Intelligence in Medicine (2017) Vol. 79, pp. 62-70
Closed Access | Times Cited: 152

Showing 26-50 of 152 citing articles:

iAFPs-EnC-GA: Identifying antifungal peptides using sequential and evolutionary descriptors based multi-information fusion and ensemble learning approach
Ashfaq Ahmad, Shahid Akbar, Muhammad Tahir, et al.
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 222, pp. 104516-104516
Closed Access | Times Cited: 69
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
Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
Phasit Charoenkwan, Wararat Chiangjong, Vannajan Sanghiran Lee, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 67
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: 53
Prediction of Antiviral peptides using transform evolutionary & SHAP analysis based descriptors by incorporation with ensemble learning strategy
Shahid Akbar, Farman Ali, Maqsood Hayat, et al.
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 230, pp. 104682-104682
Closed Access | Times Cited: 40
A novel basement membrane-related gene signature for prognosis of lung adenocarcinomas
Zhenxing Zhang, Haoran Zhu, Xiaojun Wang, et al.
Computers in Biology and Medicine (2023) Vol. 154, pp. 106597-106597
Open Access | Times Cited: 36
Identifying Neuropeptides via Evolutionary and Sequential Based Multi-Perspective Descriptors by Incorporation With Ensemble Classification Strategy
Shahid Akbar, Heba G. Mohamed, Hashim Ali, et al.
IEEE Access (2023) Vol. 11, pp. 49024-49034
Open Access | Times Cited: 32
Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides
Farman Ali, Harish Kumar, Wajdi Alghamdi, et al.
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 7, pp. 4033-4044
Open Access | Times Cited: 26
Comprehensive Analysis of Computational Methods for Predicting Anti-inflammatory Peptides
Ali Raza, Jamal Uddin, Shahid Akbar, et al.
Archives of Computational Methods in Engineering (2024) Vol. 31, Iss. 6, pp. 3211-3229
Closed Access | Times Cited: 12
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: 11
Screening ovarian cancer by using risk factors: machine learning assists
Raoof Nopour
BioMedical Engineering OnLine (2024) Vol. 23, Iss. 1
Open Access | Times Cited: 10
Empowering Glioma Prognosis With Transparent Machine Learning and Interpretative Insights Using Explainable AI
Anisha Palkar, Cifha Crecil Dias, Krishnaraj Chadaga, et al.
IEEE Access (2024) Vol. 12, pp. 31697-31718
Open 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
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
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 | Times Cited: 1
cACP: Classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components
Shahid Akbar, Ateeq Ur Rahman, Maqsood Hayat, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 196, pp. 103912-103912
Closed Access | Times Cited: 76
Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information
Muhammad Kabir, Muhammad Arif, Saeed Ahmad, et al.
Chemometrics and Intelligent Laboratory Systems (2018) Vol. 182, pp. 158-165
Closed Access | Times Cited: 69
ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation
Lijun Cai, Li Wang, Xiangzheng Fu, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 66
De novo design of anticancer peptides by ensemble artificial neural networks
Francesca Grisoni, Claudia S. Neuhaus, Miyabi Hishinuma, et al.
Journal of Molecular Modeling (2019) Vol. 25, Iss. 5
Closed Access | Times Cited: 59
Antimicrobial/Anticancer Peptides: Bioactive Molecules and Therapeutic Agents
Kimia Kardani, Azam Bolhassani
Immunotherapy (2021) Vol. 13, Iss. 8, pp. 669-684
Closed Access | Times Cited: 43
Learning embedding features based on multisense-scaled attention architecture to improve the predictive performance of anticancer peptides
Wenjia He, Yu Wang, Lizhen Cui, et al.
Bioinformatics (2021) Vol. 37, Iss. 24, pp. 4684-4693
Closed Access | Times Cited: 42
Antimicrobial peptides with anticancer activity: Today status, trends and their computational design
Masoumeh Kordi, Zeynab Borzouyi, Saideh Chitsaz, et al.
Archives of Biochemistry and Biophysics (2022) Vol. 733, pp. 109484-109484
Closed Access | Times Cited: 36
Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics
Ji Su Hwang, Seok Gi Kim, Tae Hwan Shin, et al.
Pharmaceutics (2022) Vol. 14, Iss. 5, pp. 997-997
Open Access | Times Cited: 33
ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types
Hua Deng, Meng Ding, Yimeng Wang, et al.
Computers in Biology and Medicine (2023) Vol. 158, pp. 106844-106844
Closed Access | Times Cited: 22
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

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