These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 25151146)

  • 1. Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis.
    Li R; Dong X; Ma C; Liu L
    Theor Biol Med Model; 2014 Aug; 11():37. PubMed ID: 25151146
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Integrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression.
    Li Y; Vongsangnak W; Chen L; Shen B
    BMC Med Genomics; 2014; 7 Suppl 1(Suppl 1):S3. PubMed ID: 25080090
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis.
    Gui T; Dong X; Li R; Li Y; Wang Z
    J Comput Biol; 2015 Jan; 22(1):63-71. PubMed ID: 25247452
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Pathway crosstalk analysis in prostate cancer based on protein-protein network data.
    Li HY; Jin N; Han YP; Jin XF
    Neoplasma; 2017; 64(1):22-31. PubMed ID: 27881001
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identification of molecular biomarkers for pancreatic cancer with mRMR shortest path method.
    Shen S; Gui T; Ma C
    Oncotarget; 2017 Jun; 8(25):41432-41439. PubMed ID: 28611293
    [TBL] [Abstract][Full Text] [Related]  

  • 6. In silico identification of key genes and signaling pathways targeted by a panel of signature microRNAs in prostate cancer.
    Baruah MM; Sharma N
    Med Oncol; 2019 Apr; 36(5):43. PubMed ID: 30937635
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer.
    Naorem LD; Muthaiyan M; Venkatesan A
    J Cell Biochem; 2019 Apr; 120(4):6154-6167. PubMed ID: 30302816
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis.
    Wu YP; Ke ZB; Lin F; Wen YA; Chen S; Li XD; Chen SH; Sun XL; Huang JB; Zheng QS; Xue XY; Wei Y; Xu N
    Pathol Res Pract; 2020 Oct; 216(10):153109. PubMed ID: 32853947
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Expression of microRNA-99a-3p in Prostate Cancer Based on Bioinformatics Data and Meta-Analysis of a Literature Review of 965 Cases.
    Yan HB; Zhang Y; Cen JM; Wang X; Gan BL; Huang JC; Li JY; Song QH; Li SH; Chen G
    Med Sci Monit; 2018 Jul; 24():4807-4822. PubMed ID: 29997385
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Interaction network analysis of YBX1 for identification of therapeutic targets in adenocarcinomas.
    Murugesan SN; Yadav BS; Maurya PK; Chaudhary A; Singh S; Mani A
    J Biosci; 2019 Jun; 44(2):. PubMed ID: 31180040
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identification of Potential Key Genes and Pathways in Enzalutamide-Resistant Prostate Cancer Cell Lines: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database.
    Zheng L; Dou X; Ma X; Qu W; Tang X
    Biomed Res Int; 2020; 2020():8341097. PubMed ID: 32724813
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.
    Lin Y; Chen F; Shen L; Tang X; Du C; Sun Z; Ding H; Chen J; Shen B
    J Transl Med; 2018 May; 16(1):134. PubMed ID: 29784056
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identification of prostate cancer hub genes and therapeutic agents using bioinformatics approach.
    Fang E; Zhang X; Wang Q; Wang D
    Cancer Biomark; 2017 Dec; 20(4):553-561. PubMed ID: 28800317
    [TBL] [Abstract][Full Text] [Related]  

  • 14. An approach for the identification of targets specific to bone metastasis using cancer genes interactome and gene ontology analysis.
    Vashisht S; Bagler G
    PLoS One; 2012; 7(11):e49401. PubMed ID: 23166660
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Delineating the underlying molecular mechanisms and key genes involved in metastasis of colorectal cancer via bioinformatics analysis.
    Qi C; Chen Y; Zhou Y; Huang X; Li G; Zeng J; Ruan Z; Xie X; Zhang J
    Oncol Rep; 2018 May; 39(5):2297-2305. PubMed ID: 29517105
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Screening and Identification of Key Biomarkers in Inflammatory Breast Cancer Through Integrated Bioinformatic Analyses.
    Wu J; Lv Q; Huang H; Zhu M; Meng D
    Genet Test Mol Biomarkers; 2020 Aug; 24(8):484-491. PubMed ID: 32598242
    [No Abstract]   [Full Text] [Related]  

  • 17. Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes associated with prostate cancer.
    Ye Y; Li SL; Wang SY
    PLoS One; 2018; 13(8):e0198055. PubMed ID: 30138363
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Bioinformatics Analysis of Stromal Molecular Signatures Associated with Breast and Prostate Cancer.
    Sun C; Gu Y; Chen G; Du Y
    J Comput Biol; 2019 Oct; 26(10):1130-1139. PubMed ID: 31180245
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identification of Hepatocellular Carcinoma-Related Potential Genes and Pathways Through Bioinformatic-Based Analyses.
    Wan Z; Zhang X; Luo Y; Zhao B
    Genet Test Mol Biomarkers; 2019 Nov; 23(11):766-777. PubMed ID: 31633428
    [No Abstract]   [Full Text] [Related]  

  • 20. Identification of invasion-metastasis-associated microRNAs in hepatocellular carcinoma based on bioinformatic analysis and experimental validation.
    Lou W; Chen J; Ding B; Chen D; Zheng H; Jiang D; Xu L; Bao C; Cao G; Fan W
    J Transl Med; 2018 Sep; 16(1):266. PubMed ID: 30268144
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.