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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:

Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM
Ritesh Sharma, Sameer Shrivastava, Sanjay Kumar Singh, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 46

Showing 1-25 of 46 citing articles:

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
iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks
Shahid Akbar, Quan Zou, Ali Raza, et al.
Artificial Intelligence in Medicine (2024) Vol. 151, pp. 102860-102860
Closed Access | Times Cited: 53
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities
Jing Xu, Fuyi Li, Chen Li, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Open Access | Times Cited: 42
MLACP 2.0: An updated machine learning tool for anticancer peptide prediction
Le Thi Phan, Hyun Woo Park, Thejkiran Pitti, et al.
Computational and Structural Biotechnology Journal (2022) Vol. 20, pp. 4473-4480
Open Access | Times Cited: 49
AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning
Yitian Fang, Fan Xu, Lesong Wei, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 1
Closed Access | Times Cited: 35
Prediction of antioxidant peptides using a quantitative structure−activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors
Dongya Qin, Linna Jiao, Ruihong Wang, et al.
Computers in Biology and Medicine (2023) Vol. 154, pp. 106591-106591
Closed Access | Times Cited: 33
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
Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning
Greneter Cordoves‐Delgado, César R. García‐Jacas
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 10, pp. 4310-4321
Closed Access | Times Cited: 15
Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides
Kedong Yin, Ruifang Li, Shaojie Zhang, et al.
Advanced Science (2025)
Open Access | Times Cited: 1
Deep-AVPpred: Artificial Intelligence Driven Discovery of Peptide Drugs for Viral Infections
Ritesh Sharma, Sameer Shrivastava, Sanjay Kumar Singh, et al.
IEEE Journal of Biomedical and Health Informatics (2021) Vol. 26, Iss. 10, pp. 5067-5074
Closed Access | Times Cited: 44
Do deep learning models make a difference in the identification of antimicrobial peptides?
César R. García‐Jacas, Sergio A. Pinacho-Castellanos, Luis A. García‐González, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 33
HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction
Yaqi Zhang, Gancheng Zhu, Kewei Li, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 5
Open Access | Times Cited: 32
DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning
Lantian Yao, Yuntian Zhang, Wenshuo Li, et al.
Protein Science (2023) Vol. 32, Iss. 10
Closed Access | Times Cited: 20
Examining evolutionary scale modeling‐derived different‐dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow
Karla L. Martínez‐Mauricio, César R. García‐Jacas, Greneter Cordoves‐Delgado
Protein Science (2024) Vol. 33, Iss. 4
Closed Access | Times Cited: 8
Exploring the frontiers of therapeutic breadth of antifungal peptides: A new avenue in antifungal drugs
Ihtisham Ul Haq, Sajida Maryam, Divine Yufetar Shyntum, et al.
Journal of Industrial Microbiology & Biotechnology (2024) Vol. 51
Open Access | Times Cited: 7
Classification of bioactive peptides: A systematic benchmark of models and encodings
Edoardo Bizzotto, Guido Zampieri, Laura Treu, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 2442-2452
Open Access | Times Cited: 6
Accelerating the discovery of antifungal peptides using deep temporal convolutional networks
Vishakha Singh, Sameer Shrivastava, Sanjay Kumar Singh, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Closed Access | Times Cited: 23
Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
Fernando Lobo, Maily Selena González, Alicia Boto, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 12, pp. 10270-10270
Open Access | Times Cited: 12
Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides
David Medina-Ortiz, Sebastián Contreras, Diego Fernández, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 16, pp. 8851-8851
Open Access | Times Cited: 4
TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning
Ke Yan, Hongwu Lv, Jiangyi Shao, et al.
Science China Information Sciences (2024) Vol. 67, Iss. 11
Closed Access | Times Cited: 4
1DCNN-based prediction methods for subsequent settlement of subgrade with limited monitoring data
Senlin Xie, Anfeng Hu, Meihui Wang, et al.
European Journal of Environmental and Civil engineering (2025), pp. 1-26
Closed Access
XAI-INVENT: An explainable artificial intelligence based framework for rapid discovery of novel antibiotics
Ritesh Sharma, Sameer Shrivastava, Sanjay Kumar Singh, et al.
Computers & Electrical Engineering (2025) Vol. 123, pp. 110098-110098
Closed 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
How natural language processing derived techniques are used on biological data: a systematic review
Emmanouil D. Oikonomou, Petros Karvelis, Νικόλαος Γιαννακέας, et al.
Network Modeling Analysis in Health Informatics and Bioinformatics (2024) Vol. 13, Iss. 1
Open Access | Times Cited: 3
EnDL-HemoLyt: Ensemble Deep Learning-Based Tool for Identifying Therapeutic Peptides With Low Hemolytic Activity
Ritesh Sharma, Sameer Shrivastava, Sanjay Kumar Singh, et al.
IEEE Journal of Biomedical and Health Informatics (2023) Vol. 28, Iss. 4, pp. 1896-1905
Closed Access | Times Cited: 7
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