153 related articles for article (PubMed ID: 38694921)
1. Identification of pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis based on comprehensive bioinformatics analysis and machine learning.
Qin X; Ding R; Lu H; Zhang W; Wei S; Ji B; Geng R; Wu L; Chen Z
Front Pharmacol; 2024; 15():1364160. PubMed ID: 38694921
[TBL] [Abstract][Full Text] [Related]
2. Identification of Serum Exosome-Derived circRNA-miRNA-TF-mRNA Regulatory Network in Postmenopausal Osteoporosis Using Bioinformatics Analysis and Validation in Peripheral Blood-Derived Mononuclear Cells.
Dong Q; Han Z; Tian L
Front Endocrinol (Lausanne); 2022; 13():899503. PubMed ID: 35757392
[TBL] [Abstract][Full Text] [Related]
3. Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis.
Guo J; Ning Y; Su Z; Guo L; Gu Y
BMC Med Genomics; 2022 Jun; 15(1):145. PubMed ID: 35773742
[TBL] [Abstract][Full Text] [Related]
4. Immune-associated pivotal biomarkers identification and competing endogenous RNA network construction in post-operative atrial fibrillation by comprehensive bioinformatics and machine learning strategies.
Zhou Y; Wu Q; Ni G; Hong Y; Xiao S; Liu C; Yu Z
Front Immunol; 2022; 13():974935. PubMed ID: 36341343
[TBL] [Abstract][Full Text] [Related]
5. Integrated analysis and exploration of potential shared gene signatures between carotid atherosclerosis and periodontitis.
Zeng Y; Cao S; Chen M
BMC Med Genomics; 2022 Oct; 15(1):227. PubMed ID: 36316672
[TBL] [Abstract][Full Text] [Related]
6. Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis.
Liu C; Tang L; Zhou Y; Tang X; Zhang G; Zhu Q; Zhou Y
Eur J Med Res; 2023 Feb; 28(1):92. PubMed ID: 36823662
[TBL] [Abstract][Full Text] [Related]
7. Development of gene model combined with machine learning technology to predict for advanced atherosclerotic plaques.
Wang L; Bao Y; Yu F; Zhu W; Wang JL; Yang J; Xie H; Huang D
Clin Neurol Neurosurg; 2023 Aug; 231():107819. PubMed ID: 37315377
[TBL] [Abstract][Full Text] [Related]
8. Identification of key genes for hypertrophic cardiomyopathy using integrated network analysis of differential lncRNA and gene expression.
Cao J; Yuan L
Front Cardiovasc Med; 2022; 9():946229. PubMed ID: 35990977
[TBL] [Abstract][Full Text] [Related]
9. Constructing ferroptosis-related competing endogenous RNA networks and exploring potential biomarkers correlated with immune infiltration cells in asthma using combinative bioinformatics strategy.
Chen ST; Yang N
BMC Genomics; 2023 May; 24(1):294. PubMed ID: 37259023
[TBL] [Abstract][Full Text] [Related]
10. Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning.
Liu C; Zhou Y; Zhou Y; Tang X; Tang L; Wang J
Comput Biol Med; 2023 Jan; 152():106388. PubMed ID: 36470144
[TBL] [Abstract][Full Text] [Related]
11. Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study.
Yu T; Xu B; Bao M; Gao Y; Zhang Q; Zhang X; Liu R
Front Endocrinol (Lausanne); 2022; 13():981100. PubMed ID: 36187128
[TBL] [Abstract][Full Text] [Related]
12. Identification and validation of potential diagnostic signature and immune cell infiltration for HIRI based on cuproptosis-related genes through bioinformatics analysis and machine learning.
Xiao F; Huang G; Yuan G; Li S; Wang Y; Tan Z; Liu Z; Tomlinson S; He S; Ouyang G; Zeng Y
Front Immunol; 2024; 15():1372441. PubMed ID: 38690269
[TBL] [Abstract][Full Text] [Related]
13. Identification of characteristic genes and construction of regulatory network in gallbladder carcinoma.
Shao H; Zhu J; Zhu Y; Liu L; Zhao S; Kang Q; Liu Y; Zou H
BMC Med Genomics; 2023 Oct; 16(1):240. PubMed ID: 37821907
[TBL] [Abstract][Full Text] [Related]
14. Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning.
Wang J; Kang Z; Liu Y; Li Z; Liu Y; Liu J
Front Immunol; 2022; 13():956078. PubMed ID: 36211422
[TBL] [Abstract][Full Text] [Related]
15. Molecular investigation of candidate genes for pyroptosis-induced inflammation in diabetic retinopathy.
Wang N; Ding L; Liu D; Zhang Q; Zheng G; Xia X; Xiong S
Front Endocrinol (Lausanne); 2022; 13():918605. PubMed ID: 35957838
[TBL] [Abstract][Full Text] [Related]
16. IGFBP6 Is Downregulated in Unstable Carotid Atherosclerotic Plaques According to an Integrated Bioinformatics Analysis and Experimental Verification.
Liu Y; Huan W; Wu J; Zou S; Qu L
J Atheroscler Thromb; 2020 Oct; 27(10):1068-1085. PubMed ID: 32037372
[TBL] [Abstract][Full Text] [Related]
17. Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms.
Tang Q; Su Q; Wei L; Wang K; Jiang T
Front Endocrinol (Lausanne); 2023; 14():1108616. PubMed ID: 37854191
[TBL] [Abstract][Full Text] [Related]
18. Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients.
Li Y; Liu Y; Duo M; Wu R; Jiang T; Li P; Wang Y; Cheng Z
Cell Commun Signal; 2022 Dec; 20(1):201. PubMed ID: 36575422
[TBL] [Abstract][Full Text] [Related]
19. Comprehensive analysis of an lncRNA-miRNA-mRNA competing endogenous RNA network in pulpitis.
Lei F; Zhang H; Xie X
PeerJ; 2019; 7():e7135. PubMed ID: 31304055
[TBL] [Abstract][Full Text] [Related]
20. Integrative analysis of potential biomarkers and immune cell infiltration in Parkinson's disease.
Chen X; Cao W; Zhuang Y; Chen S; Li X
Brain Res Bull; 2021 Dec; 177():53-63. PubMed ID: 34536521
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
