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:

DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
Lezheng Yu, Runyu Jing, Fengjuan Liu, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 22, pp. 862-870
Open Access | Times Cited: 72

Showing 1-25 of 72 citing articles:

Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Rohan Gupta, Devesh Srivastava, Mehar Sahu, et al.
Molecular Diversity (2021) Vol. 25, Iss. 3, pp. 1315-1360
Open Access | Times Cited: 939
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: 75
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
Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics
Kruttika Purohit, Narsimha Reddy, Anwar Sunna
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 3, pp. 1391-1391
Open Access | Times Cited: 51
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: 47
Antimicrobial resistance crisis: could artificial intelligence be the solution?
Guangyu Liu, Dan Yu, Mei-Mei Fan, et al.
Military Medical Research (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 46
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: 83
Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening
Haiping Zhang, Konda Mani Saravanan, Yanjie Wei, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 3, pp. 835-845
Open Access | Times Cited: 40
The role and future prospects of artificial intelligence algorithms in peptide drug development
Zhiheng Chen, Ruoxi Wang, Junqi Guo, et al.
Biomedicine & Pharmacotherapy (2024) Vol. 175, pp. 116709-116709
Open Access | Times Cited: 15
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
ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation
Chen Xiangan, Wen Zhang, Xiaofei Yang, et al.
Frontiers in Genetics (2021) Vol. 12
Open Access | Times Cited: 50
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
Deep learning drives efficient discovery of novel antihypertensive peptides from soybean protein isolate
Yiyun Zhang, Zijian Dai, Xinjie Zhao, et al.
Food Chemistry (2022) Vol. 404, pp. 134690-134690
Closed Access | Times Cited: 29
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
Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing
Tom Kazmirchuk, Calvin Bradbury-Jost, Taylor Ann Withey, et al.
Genes (2023) Vol. 14, Iss. 6, pp. 1194-1194
Open Access | Times Cited: 18
Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era
Liwei Chang, Arup Mondal, Bhumika Singh, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2023) Vol. 14, Iss. 1
Closed Access | Times Cited: 17
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
Machine learning for the advancement of genome-scale metabolic modeling
Pritam Kundu, Satyajit Beura, Suman Mondal, et al.
Biotechnology Advances (2024) Vol. 74, pp. 108400-108400
Closed Access | Times Cited: 7
A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence
Sanjeevi Pandiyan, Li Wang
Computers in Biology and Medicine (2022) Vol. 150, pp. 106140-106140
Closed Access | Times Cited: 26
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
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
CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction
Yitian Fang, Mingshuang Luo, Zhixiang Ren, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 5
CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework
Farid Nasiri, Fereshteh Fallah Atanaki, Saman Behrouzi, et al.
ACS Omega (2021) Vol. 6, Iss. 30, pp. 19846-19859
Open Access | Times Cited: 31
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: 22
ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree
Yanjuan Li, Ma Di, Dong Chen, et al.
Frontiers in Genetics (2023) Vol. 14
Open Access | Times Cited: 13
Page 1 - Next Page

Scroll to top