BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

198 related articles for article (PubMed ID: 28784146)

  • 1. stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage.
    Van den Berge K; Soneson C; Robinson MD; Clement L
    Genome Biol; 2017 Aug; 18(1):151. PubMed ID: 28784146
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification.
    Love MI; Soneson C; Patro R
    F1000Res; 2018; 7():952. PubMed ID: 30356428
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Two-stage designs for experiments with a large number of hypotheses.
    Zehetmayer S; Bauer P; Posch M
    Bioinformatics; 2005 Oct; 21(19):3771-7. PubMed ID: 16091414
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Quick calculation for sample size while controlling false discovery rate with application to microarray analysis.
    Liu P; Hwang JT
    Bioinformatics; 2007 Mar; 23(6):739-46. PubMed ID: 17237060
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data.
    Shedden K; Chen W; Kuick R; Ghosh D; Macdonald J; Cho KR; Giordano TJ; Gruber SB; Fearon ER; Taylor JM; Hanash S
    BMC Bioinformatics; 2005 Feb; 6():26. PubMed ID: 15705192
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures.
    Lu X; Perkins DL
    BMC Bioinformatics; 2007 May; 8():157. PubMed ID: 17509157
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies.
    Li X; Cooper NGF; O'Toole TE; Rouchka EC
    BMC Genomics; 2020 Jan; 21(1):75. PubMed ID: 31992223
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Bayesian estimation of differential transcript usage from RNA-seq data.
    Papastamoulis P; Rattray M
    Stat Appl Genet Mol Biol; 2017 Nov; 16(5-6):367-386. PubMed ID: 29091583
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparison of false discovery rate methods in identifying genes with differential expression.
    Qian HR; Huang S
    Genomics; 2005 Oct; 86(4):495-503. PubMed ID: 16054333
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data.
    Xie Y; Pan W; Khodursky AB
    Bioinformatics; 2005 Dec; 21(23):4280-8. PubMed ID: 16188930
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identifying differentially expressed genes using false discovery rate controlling procedures.
    Reiner A; Yekutieli D; Benjamini Y
    Bioinformatics; 2003 Feb; 19(3):368-75. PubMed ID: 12584122
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Optimized multi-stage designs controlling the false discovery or the family-wise error rate.
    Zehetmayer S; Bauer P; Posch M
    Stat Med; 2008 Sep; 27(21):4145-60. PubMed ID: 18444249
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multidimensional local false discovery rate for microarray studies.
    Ploner A; Calza S; Gusnanto A; Pawitan Y
    Bioinformatics; 2006 Mar; 22(5):556-65. PubMed ID: 16368770
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Sample size determination for the false discovery rate.
    Pounds S; Cheng C
    Bioinformatics; 2005 Dec; 21(23):4263-71. PubMed ID: 16204346
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data.
    Li X; Brock GN; Rouchka EC; Cooper NGF; Wu D; O'Toole TE; Gill RS; Eteleeb AM; O'Brien L; Rai SN
    PLoS One; 2017; 12(5):e0176185. PubMed ID: 28459823
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Analysis of oligonucleotide array experiments with repeated measures using mixed models.
    Li H; Wood CL; Getchell TV; Getchell ML; Stromberg AJ
    BMC Bioinformatics; 2004 Dec; 5():209. PubMed ID: 15626348
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments.
    Bi R; Liu P
    BMC Bioinformatics; 2016 Mar; 17():146. PubMed ID: 27029470
    [TBL] [Abstract][Full Text] [Related]  

  • 18. On reliable discovery of molecular signatures.
    Nilsson R; Björkegren J; Tegnér J
    BMC Bioinformatics; 2009 Jan; 10():38. PubMed ID: 19178740
    [TBL] [Abstract][Full Text] [Related]  

  • 19. High-throughput and quantitative genome-wide messenger RNA sequencing for molecular phenotyping.
    Collins JE; Wali N; Sealy IM; Morris JA; White RJ; Leonard SR; Jackson DK; Jones MC; Smerdon NC; Zamora J; Dooley CM; Carruthers SN; Barrett JC; Stemple DL; Busch-Nentwich EM
    BMC Genomics; 2015 Aug; 16(1):578. PubMed ID: 26238335
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A mixture model for estimating the local false discovery rate in DNA microarray analysis.
    Liao JG; Lin Y; Selvanayagam ZE; Shih WJ
    Bioinformatics; 2004 Nov; 20(16):2694-701. PubMed ID: 15145810
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.