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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:

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence
Stella Dimitsaki, George Gavriilidis, Vlasios K. Dimitriadis, et al.
Artificial Intelligence in Medicine (2023) Vol. 137, pp. 102490-102490
Open Access | Times Cited: 15

Showing 15 citing articles:

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey
Roohallah Alizadehsani, Solomon Sunday Oyelere, Sadiq Hussain, et al.
IEEE Access (2024) Vol. 12, pp. 35796-35812
Open Access | Times Cited: 21
Utilizing Machine Learning Models for Predicting Outcomes in Acute Pancreatitis: Development and Validation in Three Retrospective Cohorts
Kaier Gu, Yang Liu
Research Square (Research Square) (2025)
Open Access
A predictive analytics model using machine learning algorithms to estimate the risk of shock development among dengue patients
Jun Kit Chaw, Sook Hui Chaw, Chai Hoong Quah, et al.
Healthcare Analytics (2023) Vol. 5, pp. 100290-100290
Open Access | Times Cited: 11
Risk Factors and Prediction of 28-Day-All Cause Mortality Among Critically Ill Patients with Acute Pancreatitis Using Machine Learning Techniques: A Retrospective Analysis of Multi-Institutions
Weimin Cai, Xiao Wu, Yongxian Chen, et al.
Journal of Inflammation Research (2024) Vol. Volume 17, pp. 4611-4623
Open Access | Times Cited: 2
The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review
Jessica Caterson, Alex Lewin, Elizabeth Williamson
Digital Health (2024) Vol. 10
Open Access | Times Cited: 1
Transformer based on the prediction of psoriasis severity treatment response
Cho‐I Moon, Eun Bin Kim, Yoo Sang Baek, et al.
Biomedical Signal Processing and Control (2023) Vol. 89, pp. 105743-105743
Closed Access | Times Cited: 3
APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19
George Gavriilidis, Vasileios Vasileiou, Stella Dimitsaki, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Systematic benchmarking of omics computational tools
Sanjay Kumar, Manjusa Singh, Rajesh Kumar Sharma, et al.
Elsevier eBooks (2024), pp. 55-83
Closed Access
Plasma Proteins Associated with COVID-19 Severity in Puerto Rico
Lester J. Rosario-Rodríguez, Yadira M. Cantres-Rosario, Kelvin Carrasquillo, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 10, pp. 5426-5426
Open Access
Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage
Gabriel A. Vignolle, Priska Bauerstätter, Silvia Schönthaler, et al.
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 19, pp. 10304-10304
Open Access
The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model
Yanhui Liu, Wenxiu Zhang, Mengzhou Sun, et al.
BMC Pulmonary Medicine (2024) Vol. 24, Iss. 1
Open Access
Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability
André Borges Farias, Gustavo Sganzerla Martinez, Edgardo Galán-Vásquez, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 6
Open Access
In Search of Candidate Protein Biomarkers Related to COVID‐19 in Solid Tissues and Non‐Blood Fluids: An Update
Michal Alexovič, Csilla Uličná, Hadi Tabani, et al.
PROTEOMICS - CLINICAL APPLICATIONS (2024)
Closed Access
Development of a novel machine learning model based on laboratory and imaging indices to predict acute cardiac injury in cancer patients with COVID-19 infection: a retrospective observational study
G. Wan, Xuefeng Wu, Xiaowei Zhang, et al.
Journal of Cancer Research and Clinical Oncology (2023) Vol. 149, Iss. 19, pp. 17039-17050
Closed Access | Times Cited: 1
RETRACTED: Analysis and prediction of novel coronavirus pneumonia epidemic using hybrid response surface method with time-series and random forest
Li‐Ling Peng, X. Bi, Guo‐Feng Fan, et al.
Journal of Intelligent & Fuzzy Systems (2023) Vol. 46, Iss. 1, pp. 369-388
Closed Access
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