Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model
Qingwen Li, Wenyang Zhou, Donghua Wang, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 39

Showing 1-25 of 39 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
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: 42
xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning
Jiarui Chen, Hong Hin Cheong, Shirley W. I. Siu
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 8, pp. 3789-3803
Open Access | Times Cited: 80
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors
Robson Parmezan Bonidia, Douglas Silva Domingues, Danilo Sipoli Sanches, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 60
ConSIG: consistent discovery of molecular signature from OMIC data
Fengcheng Li, Jiayi Yin, Mingkun Lu, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 4
Closed Access | Times Cited: 59
Fast and High‐Strain Electrochemically Driven Yarn Actuators in Twisted and Coiled Configurations
Shazed Aziz, José G. Martínez, Bidita Salahuddin, et al.
Advanced Functional Materials (2020) Vol. 31, Iss. 10
Open Access | Times Cited: 46
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: 13
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: 5
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
ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides
Patrick Brendan Timmons, Chandralal M. Hewage
Biomedicine & Pharmacotherapy (2020) Vol. 133, pp. 111051-111051
Open Access | Times Cited: 32
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
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
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
ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information
Mingwei Sun, Sen Yang, Xuemei Hu, et al.
Molecules (2022) Vol. 27, Iss. 5, pp. 1544-1544
Open Access | Times Cited: 14
IGPred‐HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning‐Based Approach
Ali Zakir, Fahad Alturise, Tamim Alkhalifah, et al.
Computational Intelligence and Neuroscience (2023) Vol. 2023, Iss. 1
Open Access | Times Cited: 8
Identification and classification of promoters using the attention mechanism based on long short-term memory
Qingwen Li, Lichao Zhang, Lei Xu, et al.
Frontiers of Computer Science (2022) Vol. 16, Iss. 4
Closed Access | Times Cited: 13
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
Applications of peptide-based nanomaterials in targeting cancer therapy
Beilei Sun, Limin Zhang, Mengzhen Li, et al.
Biomaterials Science (2024) Vol. 12, Iss. 7, pp. 1630-1642
Closed Access | Times Cited: 2
Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum
Sita Sirisha Madugula, Pranav Pujar, Bharani Nammi, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 12, pp. 4897-4911
Open Access | Times Cited: 2
IPPF-FE: an integrated peptide and protein function prediction framework based on fused features and ensemble models
Han Yu, Xiaozhou Luo
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 12
Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network
Qingwen Li, Lei Xu, Qingyuan Li, et al.
Computational and Mathematical Methods in Medicine (2020) Vol. 2020, pp. 1-9
Open Access | Times Cited: 14
ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs
Ubaid M. Al‐Saggaf, Muhammad Usman, Imran Naseem, et al.
Frontiers in Bioengineering and Biotechnology (2021) Vol. 9
Open Access | Times Cited: 12
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
Utilize a few features to classify presynaptic and postsynaptic neurotoxins
Hao Wan, Qing Liu, Ying Ju
Computers in Biology and Medicine (2022) Vol. 152, pp. 106380-106380
Closed Access | Times Cited: 6
Page 1 - Next Page

Scroll to top