BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

165 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 9.