300 related articles for article (PubMed ID: 15513985)
1. Identifying differentially expressed genes from microarray experiments via statistic synthesis.
Yang YH; Xiao Y; Segal MR
Bioinformatics; 2005 Apr; 21(7):1084-93. PubMed ID: 15513985
[TBL] [Abstract][Full Text] [Related]
2. The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison.
Sioson AA; Mane SP; Li P; Sha W; Heath LS; Bohnert HJ; Grene R
BMC Bioinformatics; 2006 Apr; 7():215. PubMed ID: 16626497
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Bayesian hierarchical error model for analysis of gene expression data.
Cho H; Lee JK
Bioinformatics; 2004 Sep; 20(13):2016-25. PubMed ID: 15044230
[TBL] [Abstract][Full Text] [Related]
5. Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments.
Gao X
Bioinformatics; 2006 Jun; 22(12):1486-94. PubMed ID: 16574697
[TBL] [Abstract][Full Text] [Related]
6. Ranking analysis for identifying differentially expressed genes.
Qi Y; Sun H; Sun Q; Pan L
Genomics; 2011 May; 97(5):326-9. PubMed ID: 21402142
[TBL] [Abstract][Full Text] [Related]
7. Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments.
Zhao Y; Pan W
Bioinformatics; 2003 Jun; 19(9):1046-54. PubMed ID: 12801864
[TBL] [Abstract][Full Text] [Related]
8. Utilization of two sample t-test statistics from redundant probe sets to evaluate different probe set algorithms in GeneChip studies.
Hu Z; Willsky GR
BMC Bioinformatics; 2006 Jan; 7():12. PubMed ID: 16403228
[TBL] [Abstract][Full Text] [Related]
9. Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.
Seo J; Bakay M; Chen YW; Hilmer S; Shneiderman B; Hoffman EP
Bioinformatics; 2004 Nov; 20(16):2534-44. PubMed ID: 15117752
[TBL] [Abstract][Full Text] [Related]
10. A unified framework for finding differentially expressed genes from microarray experiments.
Shaik JS; Yeasin M
BMC Bioinformatics; 2007 Sep; 8():347. PubMed ID: 17877806
[TBL] [Abstract][Full Text] [Related]
11. Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data.
Breitling R; Herzyk P
J Bioinform Comput Biol; 2005 Oct; 3(5):1171-89. PubMed ID: 16278953
[TBL] [Abstract][Full Text] [Related]
12. Reproducibility-optimized test statistic for ranking genes in microarray studies.
Elo LL; Filen S; Lahesmaa R; Aittokallio T
IEEE/ACM Trans Comput Biol Bioinform; 2008; 5(3):423-431. PubMed ID: 18670045
[TBL] [Abstract][Full Text] [Related]
13. A generalized likelihood ratio test to identify differentially expressed genes from microarray data.
Wang S; Ethier S
Bioinformatics; 2004 Jan; 20(1):100-4. PubMed ID: 14693815
[TBL] [Abstract][Full Text] [Related]
14. The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.
Mansourian R; Mutch DM; Antille N; Aubert J; Fogel P; Le Goff JM; Moulin J; Petrov A; Rytz A; Voegel JJ; Roberts MA
Bioinformatics; 2004 Nov; 20(16):2726-37. PubMed ID: 15145801
[TBL] [Abstract][Full Text] [Related]
15. Hotelling's T2 multivariate profiling for detecting differential expression in microarrays.
Lu Y; Liu PY; Xiao P; Deng HW
Bioinformatics; 2005 Jul; 21(14):3105-13. PubMed ID: 15905280
[TBL] [Abstract][Full Text] [Related]
16. Identifying periodically expressed transcripts in microarray time series data.
Wichert S; Fokianos K; Strimmer K
Bioinformatics; 2004 Jan; 20(1):5-20. PubMed ID: 14693803
[TBL] [Abstract][Full Text] [Related]
17. Nonparametric methods for identifying differentially expressed genes in microarray data.
Troyanskaya OG; Garber ME; Brown PO; Botstein D; Altman RB
Bioinformatics; 2002 Nov; 18(11):1454-61. PubMed ID: 12424116
[TBL] [Abstract][Full Text] [Related]
18. Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays.
Draghici S; Kulaeva O; Hoff B; Petrov A; Shams S; Tainsky MA
Bioinformatics; 2003 Jul; 19(11):1348-59. PubMed ID: 12874046
[TBL] [Abstract][Full Text] [Related]
19. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies.
Shi L; Jones WD; Jensen RV; Harris SC; Perkins RG; Goodsaid FM; Guo L; Croner LJ; Boysen C; Fang H; Qian F; Amur S; Bao W; Barbacioru CC; Bertholet V; Cao XM; Chu TM; Collins PJ; Fan XH; Frueh FW; Fuscoe JC; Guo X; Han J; Herman D; Hong H; Kawasaki ES; Li QZ; Luo Y; Ma Y; Mei N; Peterson RL; Puri RK; Shippy R; Su Z; Sun YA; Sun H; Thorn B; Turpaz Y; Wang C; Wang SJ; Warrington JA; Willey JC; Wu J; Xie Q; Zhang L; Zhang L; Zhong S; Wolfinger RD; Tong W
BMC Bioinformatics; 2008 Aug; 9 Suppl 9(Suppl 9):S10. PubMed ID: 18793455
[TBL] [Abstract][Full Text] [Related]
20. A new outlier removal approach for cDNA microarray normalization.
Wu Y; Yan L; Liu H; Sun H; Xie H
Biotechniques; 2009 Aug; 47(2):691-2, 694-700. PubMed ID: 19737130
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]