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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:

Comprehensive Analysis of Molecular Subtypes and Hub Genes of Sepsis by Gene Expression Profiles
Yongxing Lai, Chunjin Lin, Xing Lin, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 13

Showing 13 citing articles:

Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model
Yaxue Xie, Hongshuo Shi, Bo Han
BMC Pediatrics (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 22
Identification of key genes in sepsis by WGCNA
Xuemeng Gao, Xiuhua Zhou, Meng-Wei Jia, et al.
Preventive Medicine (2023) Vol. 172, pp. 107540-107540
Open Access | Times Cited: 14
A comprehensive review of GPR84: A novel player in pathophysiology and treatment
Lingeng Zou, Xin He, Shuo Liu, et al.
International Journal of Biological Macromolecules (2025), pp. 140088-140088
Closed Access
Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis
Linfeng Tao, Yanyou Zhou, Lifang Wu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Predicting the prognosis in patients with sepsis by a pyroptosis-related gene signature
Shuang Liang, Manyu Xing, Xiang Chen, et al.
Frontiers in Immunology (2022) Vol. 13
Open Access | Times Cited: 12
Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm
Xuesong Wang, Ziyi Wang, Zhe Guo, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 19, pp. 14720-14720
Open Access | Times Cited: 6
Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification
Qian‐Fei Wang, Chenxi Wang, Weichao Zhang, et al.
Frontiers in Immunology (2023) Vol. 14
Open Access | Times Cited: 5
Molecular Subtypes and Machine Learning-Based Predictive Models for Intracranial Aneurysm Rupture
Aifang Zhong, Feichi Wang, Yang Zhou, et al.
World Neurosurgery (2023) Vol. 179, pp. e166-e186
Closed Access | Times Cited: 2
Sodium octanoate mediates GPR84-dependent and independent protection against sepsis-induced myocardial dysfunction
Yao Lin, Wenbin Zhang, Xiangkang Jiang, et al.
Biomedicine & Pharmacotherapy (2024) Vol. 180, pp. 117455-117455
Open Access
Toward precision medicine: Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome
Adolfo Ruiz-Sanmartín, Vicent Ribas, David Suñol, et al.
Research Square (Research Square) (2024)
Open Access
Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework
Chao Du, Stephanie Tan, Heng‐Fu Bu, et al.
Frontiers in Immunology (2024) Vol. 15
Open Access
Biomarkers for surgical sepsis. A review of foreign scientific and medical publications
Sergey G. Sсherbak, Аndrey М. Sarana, Dmitry A. Vologzhanin, et al.
Journal of clinical practice (2023) Vol. 14, Iss. 2, pp. 66-78
Open Access | Times Cited: 1
Identification of the Shared Gene Signatures of HCK, NOG, RNF125 and Biological Mechanism in Pediatric Acute Lymphoblastic Leukaemia and Pediatric Sepsis
Yingping Xiao, Yucai Cheng, Chun Chen, et al.
Molecular Biotechnology (2023)
Open Access | Times Cited: 1
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