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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:

iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides
Phasit Charoenkwan, Janchai Yana, Chanin Nantasenamat, et al.
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 12, pp. 6666-6678
Closed Access | Times Cited: 118

Showing 1-25 of 118 citing articles:

Characterization and molecular docking study of taste peptides from chicken soup by sensory analysis combined with nano-LC-Q-TOF-MS/MS
Li Liang, Wen Duan, Jingcheng Zhang, et al.
Food Chemistry (2022) Vol. 383, pp. 132455-132455
Closed Access | Times Cited: 138
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Bioinformatics (2021) Vol. 37, Iss. 17, pp. 2556-2562
Closed Access | Times Cited: 136
Characteristics of umami peptides identified from porcine bone soup and molecular docking to the taste receptor T1R1/T1R3
Li Liang, Chenchen Zhou, Jingcheng Zhang, et al.
Food Chemistry (2022) Vol. 387, pp. 132870-132870
Closed Access | Times Cited: 120
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides
Phasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 109
Identification of Novel Umami Peptides in Chicken Breast Soup through a Sensory-Guided Approach and Molecular Docking to the T1R1/T1R3 Taste Receptor
Lili Zhang, Dandan Pu, Jingcheng Zhang, et al.
Journal of Agricultural and Food Chemistry (2023) Vol. 71, Iss. 20, pp. 7803-7811
Closed Access | Times Cited: 72
Umami peptides screened based on peptidomics and virtual screening from Ruditapes philippinarum and Mactra veneriformis clams
Ting Zhang, Ying Hua, Changyu Zhou, et al.
Food Chemistry (2022) Vol. 394, pp. 133504-133504
Closed Access | Times Cited: 70
UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity
Zhenjiao Du, Xingjian Ding, Yixiang Xu, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Closed Access | Times Cited: 64
Virtual screening and characteristics of novel umami peptides from porcine type I collagen
Yuxiang Gu, Jingcheng Zhang, Yajie Niu, et al.
Food Chemistry (2023) Vol. 434, pp. 137386-137386
Closed Access | Times Cited: 44
Taste properties and mechanism of umami peptides from fermented goose bones based on molecular docking and molecular dynamics simulation using umami receptor T1R1/T1R3
Xinrui Feng, Ran Wang, Jingnan Lu, et al.
Food Chemistry (2024) Vol. 443, pp. 138570-138570
Closed Access | Times Cited: 33
Noteworthy Consensus Effects of D/E Residues in Umami Peptides Used for Designing the Novel Umami Peptides
Zhiyong Cui, Hengli Meng, Tianxing Zhou, et al.
Journal of Agricultural and Food Chemistry (2024) Vol. 72, Iss. 5, pp. 2789-2800
Closed Access | Times Cited: 16
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: 2
In-silico investigation of umami peptides with receptor T1R1/T1R3 for the discovering potential targets: A combined modeling approach
Wenli Wang, Zhiyong Cui, Menghua Ning, et al.
Biomaterials (2021) Vol. 281, pp. 121338-121338
Open Access | Times Cited: 78
DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach
Hao Lv, Fanny Dao, Hasan Zulfiqar, et al.
Briefings in Bioinformatics (2021)
Open Access | Times Cited: 76
ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism
Lesong Wei, Xiucai Ye, Yuyang Xue, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 74
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: 66
UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 23, pp. 13124-13124
Open Access | Times Cited: 66
A rapid selection strategy for umami peptide screening based on machine learning and molecular docking
Chen Li, Ying Hua, Daodong Pan, et al.
Food Chemistry (2022) Vol. 404, pp. 134562-134562
Closed Access | Times Cited: 61
Application of quantitative structure-activity relationship to food-derived peptides: Methods, situations, challenges and prospects
Weichen Bo, Lang Chen, Dongya Qin, et al.
Trends in Food Science & Technology (2021) Vol. 114, pp. 176-188
Closed Access | Times Cited: 60
Research progress in the screening and evaluation of umami peptides
Lulu Qi, Xinchang Gao, Daodong Pan, et al.
Comprehensive Reviews in Food Science and Food Safety (2022) Vol. 21, Iss. 2, pp. 1462-1490
Closed Access | Times Cited: 59
StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Methods (2021) Vol. 204, pp. 189-198
Closed Access | Times Cited: 58
A TastePeptides-Meta system including an umami/bitter classification model Umami_YYDS, a TastePeptidesDB database and an open-source package Auto_Taste_ML
Zhiyong Cui, Zhiwei Zhang, Tianxing Zhou, et al.
Food Chemistry (2022) Vol. 405, pp. 134812-134812
Closed Access | Times Cited: 55
SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, et al.
Computers in Biology and Medicine (2022) Vol. 146, pp. 105704-105704
Closed Access | Times Cited: 45
Umami-MRNN: Deep learning-based prediction of umami peptide using RNN and MLP
Lulu Qi, Jialuo Du, Yue Sun, et al.
Food Chemistry (2022) Vol. 405, pp. 134935-134935
Closed Access | Times Cited: 38
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: 36
Combining molecular docking and molecular dynamics simulation to discover four novel umami peptides from tuna skeletal myosin with sensory evaluation validation
Shuai Zhao, Shuang Ma, Yuanyue Zhang, et al.
Food Chemistry (2023) Vol. 433, pp. 137331-137331
Closed Access | Times Cited: 31
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