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 *

160 related articles for article (PubMed ID: 32336254)

  • 1. Covariance thresholding to detect differentially co-expressed genes from microarray gene expression data.
    Oh M; Kim K; Sun H
    J Bioinform Comput Biol; 2020 Feb; 18(1):2050002. PubMed ID: 32336254
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis.
    Culhane AC; Perrière G; Higgins DG
    BMC Bioinformatics; 2003 Nov; 4():59. PubMed ID: 14633289
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Bioinformatics analysis of differentially expressed miRNA-related mRNAs and their prognostic value in breast carcinoma.
    Zhang GM; Goyal H; Song LL
    Oncol Rep; 2018 Jun; 39(6):2865-2872. PubMed ID: 29693181
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.
    Li X; Feltus FA; Sun X; Wang JZ; Luo F
    Proteomics; 2011 Oct; 11(19):3845-52. PubMed ID: 21761563
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Fold change rank ordering statistics: a new method for detecting differentially expressed genes.
    Dembélé D; Kastner P
    BMC Bioinformatics; 2014 Jan; 15():14. PubMed ID: 24423217
    [TBL] [Abstract][Full Text] [Related]  

  • 6. [Identification of the differentially expressed genes between primary breast cancer and paired lymph node metastasis through combining mRNA differential display and gene microarray].
    Feng YM; Gao G; Zhang F; Chen H; Wan YF; Li XQ
    Zhonghua Yi Xue Za Zhi; 2006 Oct; 86(39):2749-55. PubMed ID: 17199993
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comments on: fold change rank ordering statistics: a new method for detecting differentially expressed genes.
    Dembélé D; Kastner P
    BMC Bioinformatics; 2016 Nov; 17(1):462. PubMed ID: 27846811
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Differential regulation enrichment analysis via the integration of transcriptional regulatory network and gene expression data.
    Ma S; Jiang T; Jiang R
    Bioinformatics; 2015 Feb; 31(4):563-71. PubMed ID: 25322838
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data.
    Najarian K; Zaheri M; Rad AA; Najarian S; Dargahi J
    BMC Bioinformatics; 2004 Dec; 5():201. PubMed ID: 15603585
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Questioning the utility of pooling samples in microarray experiments with cell lines.
    Lusa L; Cappelletti V; Gariboldi M; Ferrario C; De Cecco L; Reid JF; Toffanin S; Gallus G; McShane LM; Daidone MG; Pierotti MA
    Int J Biol Markers; 2006; 21(2):67-73. PubMed ID: 16847808
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An efficient method to identify differentially expressed genes in microarray experiments.
    Qin H; Feng T; Harding SA; Tsai CJ; Zhang S
    Bioinformatics; 2008 Jul; 24(14):1583-9. PubMed ID: 18453554
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.
    Galfalvy HC; Erraji-Benchekroun L; Smyrniotopoulos P; Pavlidis P; Ellis SP; Mann JJ; Sibille E; Arango V
    BMC Bioinformatics; 2003 Sep; 4():37. PubMed ID: 12962547
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A spline function approach for detecting differentially expressed genes in microarray data analysis.
    He W
    Bioinformatics; 2004 Nov; 20(17):2954-63. PubMed ID: 15180936
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Gene set enrichment analysis made simple.
    Irizarry RA; Wang C; Zhou Y; Speed TP
    Stat Methods Med Res; 2009 Dec; 18(6):565-75. PubMed ID: 20048385
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Accuracy of cDNA microarray methods to detect small gene expression changes induced by neuregulin on breast epithelial cells.
    Yao B; Rakhade SN; Li Q; Ahmed S; Krauss R; Draghici S; Loeb JA
    BMC Bioinformatics; 2004 Jul; 5():99. PubMed ID: 15272935
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Gene set internal coherence in the context of functional profiling.
    Montaner D; Minguez P; Al-Shahrour F; Dopazo J
    BMC Genomics; 2009 Apr; 10():197. PubMed ID: 19397819
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Regularized gene selection in cancer microarray meta-analysis.
    Ma S; Huang J
    BMC Bioinformatics; 2009 Jan; 10():1. PubMed ID: 19118496
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Gene set analysis using sufficient dimension reduction.
    Hsueh HM; Tsai CA
    BMC Bioinformatics; 2016 Feb; 17():74. PubMed ID: 26852017
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A non-transformation method for identifying differentially expressed genes from cDNA microarrays.
    Zhang JG; Yin ZJ; Zhang Q
    Yi Chuan Xue Bao; 2006 Jan; 33(1):80-8. PubMed ID: 16450591
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide.
    Wren JD
    Bioinformatics; 2009 Jul; 25(13):1694-701. PubMed ID: 19447786
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.