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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:

iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Analytical Biochemistry (2020) Vol. 599, pp. 113747-113747
Closed Access | Times Cited: 52

Showing 1-25 of 52 citing articles:

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
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
Informing immunotherapy with multi-omics driven machine learning
Yawei Li, Wu Xin, Deyu Fang, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 22
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
iDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method
Phasit Charoenkwan, Sakawrat Kanthawong, Chanin Nantasenamat, et al.
Journal of Proteome Research (2020) Vol. 19, Iss. 10, pp. 4125-4136
Closed Access | Times Cited: 81
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
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Journal of Computer-Aided Molecular Design (2020) Vol. 34, Iss. 10, pp. 1105-1116
Closed Access | Times Cited: 69
A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation
Chengmao Zhou, Junhong Hu, Ying Wang, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 51
A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides
Phasit Charoenkwan, Warot Chotpatiwetchkul, Vannajan Sanghiran Lee, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 41
Towards a better prediction of subcellular location of long non-coding RNA
Zhao‐Yue Zhang, Zi‐Jie Sun, Yuhe Yang, et al.
Frontiers of Computer Science (2022) Vol. 16, Iss. 5
Closed Access | Times Cited: 35
An integrative machine learning model for the identification of tumor T-cell antigens
Mir Tanveerul Hassan, Hilal Tayara, Kil To Chong
Biosystems (2024) Vol. 237, pp. 105177-105177
Closed Access | Times Cited: 6
iTTCA-RF: a random forest predictor for tumor T cell antigens
Shihu Jiao, Quan Zou, Huannan Guo, et al.
Journal of Translational Medicine (2021) Vol. 19, Iss. 1
Open Access | Times Cited: 39
iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network
Fanny Dao, Hao Lv, Wei Su, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 32
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
In Silico Methods for Assessing Cancer Immunogenicity—A Comparison Between Peptide and Protein Models
Stanislav Sotirov, Ivan Dimitrov
Applied Sciences (2025) Vol. 15, Iss. 8, pp. 4123-4123
Open Access
ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations
Mst. Shamima Khatun, Md Mehedi Hasan, Watshara Shoombuatong, et al.
Journal of Computer-Aided Molecular Design (2020) Vol. 34, Iss. 12, pp. 1229-1236
Closed Access | Times Cited: 36
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
Sequence-Based Intelligent Model for Identification of Tumor T Cell Antigens Using Fusion Features
N. Bibi, Mukhtaj Khan, Salman Khan, et al.
IEEE Access (2024) Vol. 12, pp. 155040-155051
Open Access | Times Cited: 4
In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review
Phasit Charoenkwan, Nuttapat Anuwongcharoen, Chanin Nantasenamat, et al.
Current Pharmaceutical Design (2020) Vol. 27, Iss. 18, pp. 2180-2188
Closed Access | Times Cited: 30
Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features
Yuhe R. Yang, Chi Ma, Jia-Shu Wang, et al.
Genomics (2020) Vol. 112, Iss. 6, pp. 4342-4347
Open Access | Times Cited: 27
Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing
Thi-Oanh Tran, Nguyen Quoc Khanh Le
Computers in Biology and Medicine (2024) Vol. 174, pp. 108408-108408
Closed Access | Times Cited: 3
Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines
Stanislav Sotirov, Ivan Dimitrov
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 9, pp. 4934-4934
Open Access | Times Cited: 3
TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties
Jesús Herrera‐Bravo, Lisandra Herrera Belén, Jorge G. Farías, et al.
Computational Biology and Chemistry (2021) Vol. 91, pp. 107452-107452
Closed Access | Times Cited: 22
StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors
Aijaz Ahmad Malik, Warot Chotpatiwetchkul, Chuleeporn Phanus‐umporn, et al.
Journal of Computer-Aided Molecular Design (2021) Vol. 35, Iss. 10, pp. 1037-1053
Closed Access | Times Cited: 22
PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning
Phasit Charoenkwan, Chonlatip Pipattanaboon, Chanin Nantasenamat, et al.
Computers in Biology and Medicine (2022) Vol. 152, pp. 106368-106368
Closed Access | Times Cited: 14
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