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Journal Abstract Search

226 related items for PubMed ID: 29705125

  • 1. Systematically analyses of the common dysregulated networks to understand the common pathologies between T2D and atherosclerosis.
    Lin Z, Yang F, Sun L, Gao J, Cao Y, Qiu H, Zhan X.
    Gene; 2018 Sep 10; 671():110-116. PubMed ID: 29705125
    [Abstract] [Full Text] [Related]

  • 2. Unveiling the bioinformatic genes and their involved regulatory mechanisms in type 2 diabetes combined with osteoarthritis.
    Mao G, Xu W, Wan L, Wang H, Xu S, Zhang L, Li S, Zhang J, Lai Z, Lan Y, Liu J.
    Front Immunol; 2024 Sep 10; 15():1353915. PubMed ID: 39176085
    [Abstract] [Full Text] [Related]

  • 3. Integrated analysis of microRNA and gene expression profiles reveals a functional regulatory module associated with liver fibrosis.
    Chen W, Zhao W, Yang A, Xu A, Wang H, Cong M, Liu T, Wang P, You H.
    Gene; 2017 Dec 15; 636():87-95. PubMed ID: 28919164
    [Abstract] [Full Text] [Related]

  • 4. A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases.
    Rahman MH, Peng S, Hu X, Chen C, Rahman MR, Uddin S, Quinn JMW, Moni MA.
    Int J Environ Res Public Health; 2020 Feb 06; 17(3):. PubMed ID: 32041280
    [Abstract] [Full Text] [Related]

  • 5. Integrated analysis of the gene expression profile and DNA methylation profile of obese patients with type 2 diabetes.
    Shen J, Zhu B.
    Mol Med Rep; 2018 Jun 06; 17(6):7636-7644. PubMed ID: 29620215
    [Abstract] [Full Text] [Related]

  • 6. Computational analyses of type 2 diabetes-associated loci identified by genome-wide association studies.
    Cheng M, Liu X, Yang M, Han L, Xu A, Huang Q.
    J Diabetes; 2017 Apr 06; 9(4):362-377. PubMed ID: 27121852
    [Abstract] [Full Text] [Related]

  • 7. Network analysis of atherosclerotic genes elucidates druggable targets.
    Banik SK, Baishya S, Das Talukdar A, Choudhury MD.
    BMC Med Genomics; 2022 Mar 03; 15(1):42. PubMed ID: 35241081
    [Abstract] [Full Text] [Related]

  • 8. Identification of biological targets of therapeutic intervention for diabetic nephropathy with bioinformatics approach.
    Wu T, Li Q, Wu T, Liu HY.
    Exp Clin Endocrinol Diabetes; 2014 Nov 03; 122(10):587-91. PubMed ID: 25003364
    [Abstract] [Full Text] [Related]

  • 9. 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 16; 15(1):63. PubMed ID: 28302149
    [Abstract] [Full Text] [Related]

  • 10. Shared molecular pathways and gene networks for cardiovascular disease and type 2 diabetes mellitus in women across diverse ethnicities.
    Chan KH, Huang YT, Meng Q, Wu C, Reiner A, Sobel EM, Tinker L, Lusis AJ, Yang X, Liu S.
    Circ Cardiovasc Genet; 2014 Dec 16; 7(6):911-9. PubMed ID: 25371518
    [Abstract] [Full Text] [Related]

  • 11. Combining bioinformatics, network pharmacology and artificial intelligence to predict the mechanism of celastrol in the treatment of type 2 diabetes.
    Wu M, Zhang Y.
    Front Endocrinol (Lausanne); 2022 Dec 16; 13():1030278. PubMed ID: 36339449
    [Abstract] [Full Text] [Related]

  • 12. Transcriptome analyses identify key genes and potential mechanisms in a rat model of osteoarthritis.
    Li HZ, Lu HD.
    J Orthop Surg Res; 2018 Dec 14; 13(1):319. PubMed ID: 30551734
    [Abstract] [Full Text] [Related]

  • 13. Gene-microRNA network module analysis for ovarian cancer.
    Zhang S, Ng MK.
    BMC Syst Biol; 2016 Dec 23; 10(Suppl 4):117. PubMed ID: 28155675
    [Abstract] [Full Text] [Related]

  • 14. Identification of rifampin-regulated functional modules and related microRNAs in human hepatocytes based on the protein interaction network.
    Li J, Wang Y, Wang L, Dai X, Cong W, Feng W, Xu C, Deng Y, Wang Y, Skaar TC, Liang H, Liu Y.
    BMC Genomics; 2016 Aug 22; 17 Suppl 7(Suppl 7):517. PubMed ID: 27557147
    [Abstract] [Full Text] [Related]

  • 15. Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes.
    Hale PJ, López-Yunez AM, Chen JY.
    BMC Syst Biol; 2012 Aug 22; 6 Suppl 3(Suppl 3):S16. PubMed ID: 23281828
    [Abstract] [Full Text] [Related]

  • 16. Identification of Potentially Functional CircRNA-miRNA-mRNA Regulatory Network in Hepatocellular Carcinoma by Integrated Microarray Analysis.
    Lin X, Chen Y.
    Med Sci Monit Basic Res; 2018 Apr 30; 24():70-78. PubMed ID: 29706616
    [Abstract] [Full Text] [Related]

  • 17. Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites.
    Ammah AA, Do DN, Bissonnette N, Gévry N, Ibeagha-Awemu EM.
    Int J Mol Sci; 2018 Aug 24; 19(9):. PubMed ID: 30149509
    [Abstract] [Full Text] [Related]

  • 18. Exploring the pathogenetic association between schizophrenia and type 2 diabetes mellitus diseases based on pathway analysis.
    Liu Y, Li Z, Zhang M, Deng Y, Yi Z, Shi T.
    BMC Med Genomics; 2013 Aug 24; 6 Suppl 1(Suppl 1):S17. PubMed ID: 23369358
    [Abstract] [Full Text] [Related]

  • 19. Exploration of shared gene signatures and molecular mechanisms between type 2 diabetes and osteoporosis.
    Du A, Xu R, Yang Q, Lu Y, Luo X.
    J Cell Mol Med; 2024 May 24; 28(9):e18141. PubMed ID: 38742851
    [Abstract] [Full Text] [Related]

  • 20. Convergence and divergence of genetic and modular networks between diabetes and breast cancer.
    Zhang X, Zhang Y, Yu Y, Liu J, Yuan Y, Zhao Y, Li H, Wang J, Wang Z.
    J Cell Mol Med; 2015 May 24; 19(5):1094-102. PubMed ID: 25752479
    [Abstract] [Full Text] [Related]

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