BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

240 related articles for article (PubMed ID: 31292051)

  • 41. Biological information analysis of differentially expressed genes in oral squamous cell carcinoma tissues in GEO database.
    Wang Y; Fan H; Zheng L
    J BUON; 2018; 23(6):1662-1670. PubMed ID: 30610792
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Identification of key genes and pathways in pelvic organ prolapse based on gene expression profiling by bioinformatics analysis.
    Zhou Q; Hong L; Wang J
    Arch Gynecol Obstet; 2018 May; 297(5):1323-1332. PubMed ID: 29546564
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma.
    Sang L; Wang XM; Xu DY; Zhao WJ
    World J Gastroenterol; 2018 Jun; 24(24):2605-2616. PubMed ID: 29962817
    [TBL] [Abstract][Full Text] [Related]  

  • 44. 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]  

  • 45. Identification of Potential Transcriptional Biomarkers Differently Expressed in Both
    Chen H; Li Y; Li T; Sun H; Tan C; Gao M; Xing W; Xiao X
    Biomed Res Int; 2019; 2019():2487921. PubMed ID: 31093495
    [TBL] [Abstract][Full Text] [Related]  

  • 46. 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]  

  • 47. Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation.
    Li Z; Zhang Y; Liu Y; Liu Y; Li Y
    Mol Med Rep; 2017 Sep; 16(3):3133-3146. PubMed ID: 28714002
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Exploring the molecular mechanism of sepsis-associated encephalopathy by integrated analysis of multiple datasets.
    Zhang Q; Lu C; Fan W; Yin Y
    Cytokine; 2024 Aug; 180():156609. PubMed ID: 38781871
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Bioinformatic identification of candidate biomarkers and related transcription factors in nasopharyngeal carcinoma.
    Ye Z; Wang F; Yan F; Wang L; Li B; Liu T; Hu F; Jiang M; Li W; Fu Z
    World J Surg Oncol; 2019 Apr; 17(1):60. PubMed ID: 30935420
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis.
    Fu Q; Yu W; Fu S; Chen E; Zhang S; Liang TB
    Medicine (Baltimore); 2020 Jul; 99(27):e20759. PubMed ID: 32629654
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Genes related to inflammation and bone loss process in periodontitis suggested by bioinformatics methods.
    Song L; Yao J; He Z; Xu B
    BMC Oral Health; 2015 Sep; 15():105. PubMed ID: 26334995
    [TBL] [Abstract][Full Text] [Related]  

  • 52. High-efficient Screening Method for Identification of Key Genes in Breast Cancer Through Microarray and Bioinformatics.
    Liu Z; Liang G; Tan L; Su AN; Jiang W; Gong C
    Anticancer Res; 2017 Aug; 37(8):4329-4335. PubMed ID: 28739725
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Identification and interaction analysis of key genes and microRNAs in hepatocellular carcinoma by bioinformatics analysis.
    Mou T; Zhu D; Wei X; Li T; Zheng D; Pu J; Guo Z; Wu Z
    World J Surg Oncol; 2017 Mar; 15(1):63. PubMed ID: 28302149
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Identification of Potential Key Genes Associated with Adipogenesis through Integrated Analysis of Five Mouse Transcriptome Datasets.
    Zhang S; Wang L; Li S; Zhang W; Ma X; Cheng G; Yang W; Zan L
    Int J Mol Sci; 2018 Nov; 19(11):. PubMed ID: 30424473
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Identification of breast cancer hub genes and analysis of prognostic values using integrated bioinformatics analysis.
    Fang E; Zhang X
    Cancer Biomark; 2017 Dec; 21(1):373-381. PubMed ID: 29081411
    [TBL] [Abstract][Full Text] [Related]  

  • 56. [Analysis of sepsis-related genes through weighted gene co-expression network].
    Chen C; Li L; Zhao C; Zhen J; Yan J
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2021 Jun; 33(6):659-664. PubMed ID: 34296682
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Comparative gene expression profiling reveals key pathways in septic skeletal muscle.
    Xu HZ; Wang MT; Mei B; Wang JL; He J
    Eur Rev Med Pharmacol Sci; 2013 Nov; 17(21):2867-73. PubMed ID: 24254554
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Bioinformatics analysis of gene expression profile data to screen key genes involved in pulmonary sarcoidosis.
    Li H; Zhao X; Wang J; Zong M; Yang H
    Gene; 2017 Jan; 596():98-104. PubMed ID: 27682024
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Bioinformatics analysis of fibroblasts exposed to TGF‑β at the early proliferation phase of wound repair.
    Mi B; Liu G; Zhou W; Lv H; Zha K; Liu Y; Wu Q; Liu J
    Mol Med Rep; 2017 Dec; 16(6):8146-8154. PubMed ID: 28983581
    [TBL] [Abstract][Full Text] [Related]  

  • 60. [Bioinformatic Analysis of Differentially Expressed Genes Involved in the Post-hypoxic Ischemic Brain Damage of Newborn Rats].
    Shi J; Tang J; Mu DZ
    Sichuan Da Xue Xue Bao Yi Xue Ban; 2016 Sep; 47(5):722-726. PubMed ID: 28598087
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 12.