BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

785 related articles for article (PubMed ID: 26944061)

  • 1. Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease.
    Liu J; Jing L; Tu X
    BMC Cardiovasc Disord; 2016 Mar; 16():54. PubMed ID: 26944061
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Coronary artery disease associated specific modules and feature genes revealed by integrative methods of WGCNA, MetaDE and machine learning.
    Wang Y; Liu T; Liu Y; Chen J; Xin B; Wu M; Cui W
    Gene; 2019 Aug; 710():122-130. PubMed ID: 31075415
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Analysis of Differentially Expressed Genes in Coronary Artery Disease by Integrated Microarray Analysis.
    Balashanmugam MV; Shivanandappa TB; Nagarethinam S; Vastrad B; Vastrad C
    Biomolecules; 2019 Dec; 10(1):. PubMed ID: 31881747
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identification of key miRNAs and mRNAs related to coronary artery disease by meta-analysis.
    Liu L; Zhang J; Wu M; Xu H
    BMC Cardiovasc Disord; 2021 Sep; 21(1):443. PubMed ID: 34530741
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy.
    Li X; Wang C; Zhang X; Liu J; Wang Y; Li C; Guo D
    Hereditas; 2020 Oct; 157(1):42. PubMed ID: 33099311
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.
    Ma C; Lv Q; Teng S; Yu Y; Niu K; Yi C
    Int J Rheum Dis; 2017 Aug; 20(8):971-979. PubMed ID: 28440025
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease.
    Zheng PF; Chen LZ; Guan YZ; Liu P
    Sci Rep; 2021 Mar; 11(1):6711. PubMed ID: 33758323
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Integrated microRNA‑gene analysis of coronary artery disease based on miRNA and gene expression profiles.
    Xu X; Li H
    Mol Med Rep; 2016 Apr; 13(4):3063-73. PubMed ID: 26936111
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Potentially critical roles of TNPO1, RAP1B, ZDHHC17, and PPM1B in the progression of coronary atherosclerosis through microarray data analysis.
    Zhang X; Sun R; Liu L
    J Cell Biochem; 2019 Mar; 120(3):4301-4311. PubMed ID: 30269354
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Identification of key genes and crucial modules associated with coronary artery disease by bioinformatics analysis.
    Zhang X; Cheng X; Liu H; Zheng C; Rao K; Fang Y; Zhou H; Xiong S
    Int J Mol Med; 2014 Sep; 34(3):863-9. PubMed ID: 24969630
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Exploration and validation of hub genes and pathways in the progression of hypoplastic left heart syndrome via weighted gene co-expression network analysis.
    Liu X; Shang H; Li B; Zhao L; Hua Y; Wu K; Hu M; Fan T
    BMC Cardiovasc Disord; 2021 Jun; 21(1):300. PubMed ID: 34130651
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Identification of candidate genes for necrotizing enterocolitis based on microarray data.
    Chen G; Li Y; Su Y; Zhou L; Zhang H; Shen Q; Du C; Li H; Wen Z; Xia Y; Tang W
    Gene; 2018 Jun; 661():152-159. PubMed ID: 29605607
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis.
    Li Y; He XN; Li C; Gong L; Liu M
    Biomed Res Int; 2019; 2019():5742608. PubMed ID: 30886860
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Integrative analysis of promising molecular biomarkers and pathways for coronary artery disease using WGCNA and MetaDE methods.
    Yan S
    Mol Med Rep; 2018 Sep; 18(3):2789-2797. PubMed ID: 30015926
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Identification of susceptibility modules for coronary artery disease using a genome wide integrated network analysis.
    Duan S; Luo X; Dong C
    Gene; 2013 Dec; 531(2):347-54. PubMed ID: 23994195
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identification of candidate target genes for human peripheral arterial disease using weighted gene co‑expression network analysis.
    Yin DX; Zhao HM; Sun DJ; Yao J; Ding DY
    Mol Med Rep; 2015 Dec; 12(6):8107-12. PubMed ID: 26498853
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identification of therapeutic targets for Alzheimer's disease via differentially expressed gene and weighted gene co-expression network analyses.
    Jia Y; Nie K; Li J; Liang X; Zhang X
    Mol Med Rep; 2016 Nov; 14(5):4844-4848. PubMed ID: 27748870
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.
    Peng W; Sun Y; Zhang L
    BMC Cardiovasc Disord; 2022 Feb; 22(1):42. PubMed ID: 35151267
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identification of the susceptible genes and mechanism underlying the comorbid presence of coronary artery disease and rheumatoid arthritis: a network modularization analysis.
    Zhang S; Niu Q; Tong L; Liu S; Wang P; Xu H; Li B; Zhang H
    BMC Genomics; 2023 Jul; 24(1):411. PubMed ID: 37474895
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Gene expression profiling of CD133-positive cells in coronary artery disease.
    Li J; Zhou C; Li J; Wan Y; Li T; Ma P; Wang Y; Sang H
    Mol Med Rep; 2015 Nov; 12(5):7512-6. PubMed ID: 26458356
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 40.