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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:

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: 60

Showing 1-25 of 60 citing articles:

Review and perspective on bioactive peptides: A roadmap for research, development, and future opportunities
Zhenjiao Du, Yonghui Li
Journal of Agriculture and Food Research (2022) Vol. 9, pp. 100353-100353
Open Access | Times Cited: 66
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: 46
TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization
Young-Jun Jeon, Md Mehedi Hasan, Hyun Woo Park, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 4
Open Access | Times Cited: 42
Strengths and limitations of in silico tools to assess physicochemical properties, bioactivity, and bioavailability of food-derived peptides
Fernando Rivero‐Pino, María C. Millán-Linares, Sergio Montserrat‐de la Paz
Trends in Food Science & Technology (2023) Vol. 138, pp. 433-440
Closed Access | Times Cited: 28
Deep2Pep: A Deep Learning Method in Multi-label Classification of Bioactive Peptide
Lihua Chen, Zhenkang Hu, Yuzhi Rong, et al.
Computational Biology and Chemistry (2024), pp. 108021-108021
Closed Access | Times Cited: 10
Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction
Watshara Shoombuatong, Nutta Homdee, Nalini Schaduangrat, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 10
Stack-HDAC3i: A high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework
Watshara Shoombuatong, Ittipat Meewan, Lawankorn Mookdarsanit, et al.
Methods (2024) Vol. 230, pp. 147-157
Closed Access | Times Cited: 9
Microalga Nannochloropsis gaditana as a Sustainable Source of Bioactive Peptides: A Proteomic and In Silico Approach
Samuel Paterson, Laura Alonso-Pintre, Esperanza Morato, et al.
Foods (2025) Vol. 14, Iss. 2, pp. 252-252
Open Access | Times Cited: 1
Advancing the accuracy of tyrosinase inhibitory peptides prediction via a multiview feature fusion strategy
Watshara Shoombuatong, Nalini Schaduangrat, Nutta Homdee, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins
Saeed Ahmad, Phasit Charoenkwan, Julian M.W. Quinn, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 33
Activity, structural features and in silico digestion of antidiabetic peptides
Carmen Berraquero‐García, Fernando Rivero‐Pino, Jimena Ospina, et al.
Food Bioscience (2023) Vol. 55, pp. 102954-102954
Open Access | Times Cited: 19
Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework
Phasit Charoenkwan, Nalini Schaduangrat, Píetro Lió, et al.
iScience (2022) Vol. 25, Iss. 9, pp. 104883-104883
Open Access | Times Cited: 24
Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications
Sirui Liang, Yanxi Zhao, Junru Jin, et al.
Computers in Biology and Medicine (2023) Vol. 164, pp. 107238-107238
Closed Access | Times Cited: 14
Stack-AVP: a stacked ensemble predictor based on multi-view information for fast and accurate discovery of antiviral peptides
Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Journal of Molecular Biology (2024), pp. 168853-168853
Closed Access | Times Cited: 6
Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis
Yiyun Zhang, Yiqing Zhu, Xin Bao, et al.
Research (2024) Vol. 7
Open Access | Times Cited: 4
StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features
Muhammad Arif, Saleh Musleh, Ali Ghulam, et al.
Methods (2024) Vol. 230, pp. 129-139
Open Access | Times Cited: 4
Deepstack-ACE: A deep stacking-based ensemble learning framework for the accelerated discovery of ACE inhibitory peptides
Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Methods (2024)
Closed Access | Times Cited: 4
NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides
Phasit Charoenkwan, Nalini Schaduangrat, Píetro Lió, et al.
Computers in Biology and Medicine (2022) Vol. 148, pp. 105700-105700
Closed Access | Times Cited: 19
Characterization of Rugulopteryx okamurae algae: A source of bioactive peptides, omega-3 fatty acids, and volatile compounds
Fernando Rivero‐Pino, Teresa Gonzalez‐de la Rosa, María Torrecillas-López, et al.
Food Chemistry (2025), pp. 143084-143084
Closed Access
REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset
Le Thi Phan, Rajan Rakkiyappan, Balachandran Manavalan
Computers in Biology and Medicine (2025) Vol. 187, pp. 109771-109771
Closed Access
Discovery of novel DPP4 inhibitory peptides from egg yolk by machine learning and molecular docking: In vitro and in vivo validation
Yu-Jie Xu, Yiqiao Pei, Zhifu Liu, et al.
Food Chemistry (2025) Vol. 476, pp. 143412-143412
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
A comprehensive review and evaluation of machine learning-based approaches for identifying tumor T cell antigens
Watshara Shoombuatong, Saeed Ahmed, Sakib Mahmud, et al.
Computational Biology and Chemistry (2025), pp. 108440-108440
Closed Access
M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy
Nalini Schaduangrat, Hathaichanok Chuntakaruk, Thanyada Rungrotmongkol, et al.
BMC Bioinformatics (2025) Vol. 26, Iss. 1
Open Access
PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning
Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, et al.
Computers in Biology and Medicine (2023) Vol. 158, pp. 106784-106784
Closed Access | Times Cited: 9
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