BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

308 related articles for article (PubMed ID: 24564186)

  • 1. Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq.
    Miller JA; Menon V; Goldy J; Kaykas A; Lee CK; Smith KA; Shen EH; Phillips JW; Lein ES; Hawrylycz MJ
    BMC Genomics; 2014 Feb; 15(1):154. PubMed ID: 24564186
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Seq-ing improved gene expression estimates from microarrays using machine learning.
    Korir PK; Geeleher P; Seoighe C
    BMC Bioinformatics; 2015 Sep; 16():286. PubMed ID: 26338512
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Correlation between RNA-Seq and microarrays results using TCGA data.
    Chen L; Sun F; Yang X; Jin Y; Shi M; Wang L; Shi Y; Zhan C; Wang Q
    Gene; 2017 Sep; 628():200-204. PubMed ID: 28734892
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling.
    Castillo D; Gálvez JM; Herrera LJ; Román BS; Rojas F; Rojas I
    BMC Bioinformatics; 2017 Nov; 18(1):506. PubMed ID: 29157215
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Using microarray-based subtyping methods for breast cancer in the era of high-throughput RNA sequencing.
    Pedersen CB; Nielsen FC; Rossing M; Olsen LR
    Mol Oncol; 2018 Dec; 12(12):2136-2146. PubMed ID: 30289602
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Estimating accuracy of RNA-Seq and microarrays with proteomics.
    Fu X; Fu N; Guo S; Yan Z; Xu Y; Hu H; Menzel C; Chen W; Li Y; Zeng R; Khaitovich P
    BMC Genomics; 2009 Apr; 10():161. PubMed ID: 19371429
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparing bioinformatic gene expression profiling methods: microarray and RNA-Seq.
    Mantione KJ; Kream RM; Kuzelova H; Ptacek R; Raboch J; Samuel JM; Stefano GB
    Med Sci Monit Basic Res; 2014 Aug; 20():138-42. PubMed ID: 25149683
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays.
    Bottomly D; Walter NA; Hunter JE; Darakjian P; Kawane S; Buck KJ; Searles RP; Mooney M; McWeeney SK; Hitzemann R
    PLoS One; 2011 Mar; 6(3):e17820. PubMed ID: 21455293
    [TBL] [Abstract][Full Text] [Related]  

  • 9. RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice.
    Nault R; Fader KA; Zacharewski T
    BMC Genomics; 2015 May; 16(1):373. PubMed ID: 25958198
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease.
    Raghavachari N; Barb J; Yang Y; Liu P; Woodhouse K; Levy D; O'Donnell CJ; Munson PJ; Kato GJ
    BMC Med Genomics; 2012 Jun; 5():28. PubMed ID: 22747986
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin.
    Wang G; Kitaoka T; Crawford A; Mao Q; Hesketh A; Guppy FM; Ash GI; Liu J; Gerstein MB; Pitsiladis YP
    Sci Rep; 2021 Nov; 11(1):21705. PubMed ID: 34737331
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Transcriptional profiling of endocrine cerebro-osteodysplasia using microarray and next-generation sequencing.
    Lahiry P; Lee LJ; Frey BJ; Rupar CA; Siu VM; Blencowe BJ; Hegele RA
    PLoS One; 2011; 6(9):e25400. PubMed ID: 21980446
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Analysis of Microarray and RNA-seq Expression Profiling Data.
    Hung JH; Weng Z
    Cold Spring Harb Protoc; 2017 Mar; 2017(3):. PubMed ID: 27574194
    [TBL] [Abstract][Full Text] [Related]  

  • 14. EXPRSS: an Illumina based high-throughput expression-profiling method to reveal transcriptional dynamics.
    Rallapalli G; Kemen EM; Robert-Seilaniantz A; Segonzac C; Etherington GJ; Sohn KH; MacLean D; Jones JD
    BMC Genomics; 2014 May; 15(1):341. PubMed ID: 24884414
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A large-scale comparative study of isoform expressions measured on four platforms.
    Zhang W; Petegrosso R; Chang JW; Sun J; Yong J; Chien J; Kuang R
    BMC Genomics; 2020 Mar; 21(1):272. PubMed ID: 32228441
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.
    Esnaola M; Puig P; Gonzalez D; Castelo R; Gonzalez JR
    BMC Bioinformatics; 2013 Aug; 14():254. PubMed ID: 23965047
    [TBL] [Abstract][Full Text] [Related]  

  • 17. RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.
    Sîrbu A; Kerr G; Crane M; Ruskin HJ
    PLoS One; 2012; 7(12):e50986. PubMed ID: 23251411
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements.
    Carter SL; Eklund AC; Mecham BH; Kohane IS; Szallasi Z
    BMC Bioinformatics; 2005 Apr; 6():107. PubMed ID: 15850491
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Genes, behavior and next-generation RNA sequencing.
    Hitzemann R; Bottomly D; Darakjian P; Walter N; Iancu O; Searles R; Wilmot B; McWeeney S
    Genes Brain Behav; 2013 Feb; 12(1):1-12. PubMed ID: 23194347
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species.
    Liu S; Lin L; Jiang P; Wang D; Xing Y
    Nucleic Acids Res; 2011 Jan; 39(2):578-88. PubMed ID: 20864445
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.