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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:

IUP-BERT: Identification of Umami Peptides Based on BERT Features
Liangzhen Jiang, Jici Jiang, Xiao Wang, et al.
Foods (2022) Vol. 11, Iss. 22, pp. 3742-3742
Open Access | Times Cited: 18

Showing 18 citing articles:

Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development
Zhiyong Cui, Chong Qi, Tianxing Zhou, et al.
Comprehensive Reviews in Food Science and Food Safety (2025) Vol. 24, Iss. 1
Closed Access | Times Cited: 3
Conserved Sites and Recognition Mechanisms of T1R1 and T2R14 Receptors Revealed by Ensemble Docking and Molecular Descriptors and Fingerprints Combined with Machine Learning
Zhiyong Cui, Ninglong Zhang, Tianxing Zhou, et al.
Journal of Agricultural and Food Chemistry (2023) Vol. 71, Iss. 14, pp. 5630-5645
Closed Access | Times Cited: 37
ACP-CLB: An Anticancer Peptide Prediction Model Based on Multichannel Discriminative Processing and Integration of Large Pretrained Protein Language Models
Aoyun Geng, Zhenjie Luo, Aohan Li, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access | Times Cited: 1
Identification of Thermophilic Proteins Based on Sequence-Based Bidirectional Representations from Transformer-Embedding Features
Hongdi Pei, Jiayu Li, Shuhan Ma, et al.
Applied Sciences (2023) Vol. 13, Iss. 5, pp. 2858-2858
Open Access | Times Cited: 20
FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization
Jici Jiang, Hongdi Pei, Jiayu Li, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 2
Open Access | Times Cited: 7
msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths
Yazi Li, Xiaoman Wei, Qinglin Yang, et al.
BMC Biology (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 7
Exploring the Relationship between Small Peptides and the T1R1/T1R3 Umami Taste Receptor for Umami Peptide Prediction: A Combined Approach
Wenyuan Zhang, Hui Guan, Miaomiao Wang, et al.
Journal of Agricultural and Food Chemistry (2024) Vol. 72, Iss. 23, pp. 13262-13272
Closed Access | Times Cited: 5
Screening and identification of novel umami peptides from yeast proteins: Insights into their mechanism of action on receptors T1R1/T1R3
Yuxiang Gu, Xuewei Zhou, Yajie Niu, et al.
Food Chemistry (2024) Vol. 463, pp. 141138-141138
Closed Access | Times Cited: 5
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
From prediction to design: Revealing the mechanisms of umami peptides using interpretable deep learning, quantum chemical simulations, and module substitution
Lijun Su, Zongwei Ma, Huizhuo Ji, et al.
Food Chemistry (2025), pp. 144301-144301
Closed Access
A Machine Learning Method to Identify Umami Peptide Sequences by Using Multiplicative LSTM Embedded Features
Jici Jiang, Jiayu Li, Junxian Li, et al.
Foods (2023) Vol. 12, Iss. 7, pp. 1498-1498
Open Access | Times Cited: 10
Bioinformatics and bioactive peptides from foods: Do they work together?
Anna Iwaniak, Piotr Mińkiewicz, Małgorzata Darewicz
Advances in food and nutrition research (2024), pp. 35-111
Closed Access | Times Cited: 2
UmamiPreDL: Deep Learning model for umami taste prediction of peptides using BERT and CNN
Arun Pandiyan Indiran, Humaira Fatima, S. Chattopadhyay, et al.
Computational Biology and Chemistry (2024) Vol. 111, pp. 108116-108116
Closed Access | Times Cited: 2
Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides
Yiting Deng, Shuhan Ma, Jiayu Li, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 13, pp. 10854-10854
Open Access | Times Cited: 5
Recent progress and prospects in production and identification of umami peptides from marine proteins
Di Hu, Zhenxiao Zheng, Botao Liang, et al.
Food Innovation and Advances (2024) Vol. 3, Iss. 3, pp. 256-267
Open Access | Times Cited: 1
Umami-gcForest: Construction of a predictive model for umami peptides based on deep forest
Shuaiqi Ji, Junrui Wu, Feiyu An, et al.
Food Chemistry (2024) Vol. 464, pp. 141826-141826
Closed Access | Times Cited: 1
An Effective Analysis and Exploration of Cutting-Edge Machine Learning for Protein Structure and Sequence Prediction
Afaque Alam, Mukesh Kumar
SN Computer Science (2024) Vol. 5, Iss. 6
Closed Access
Understanding and Therapeutic Application of Immune Response in Major Histocompatibility Complex (MHC) Diversity Using Multimodal Artificial Intelligence
Yasunari Matsuzaka, Ryu Yashiro
BioMedInformatics (2024) Vol. 4, Iss. 3, pp. 1835-1864
Open Access
PTSP-BERT: Predict the thermal stability of proteins using sequence-based bidirectional representations from transformer-embedded features
Zhibin Lv, Mingxuan Wei, Hongdi Pei, et al.
Computers in Biology and Medicine (2024) Vol. 185, pp. 109598-109598
Closed Access
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