444 related articles for article (PubMed ID: 21402142)
1. 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]
2. 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]
3. 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]
4. Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis.
Nettleton D; Recknor J; Reecy JM
Bioinformatics; 2008 Jan; 24(2):192-201. PubMed ID: 18042553
[TBL] [Abstract][Full Text] [Related]
5. Tumor classification ranking from microarray data.
Hewett R; Kijsanayothin P
BMC Genomics; 2008 Sep; 9 Suppl 2(Suppl 2):S21. PubMed ID: 18831787
[TBL] [Abstract][Full Text] [Related]
6. The influence of missing value imputation on detection of differentially expressed genes from microarray data.
Scheel I; Aldrin M; Glad IK; Sørum R; Lyng H; Frigessi A
Bioinformatics; 2005 Dec; 21(23):4272-9. PubMed ID: 16216830
[TBL] [Abstract][Full Text] [Related]
7. Cross platform microarray analysis for robust identification of differentially expressed genes.
Bosotti R; Locatelli G; Healy S; Scacheri E; Sartori L; Mercurio C; Calogero R; Isacchi A
BMC Bioinformatics; 2007 Mar; 8 Suppl 1(Suppl 1):S5. PubMed ID: 17430572
[TBL] [Abstract][Full Text] [Related]
8. A statistical method for estimating the proportion of differentially expressed genes.
Lai Y
Comput Biol Chem; 2006 Jun; 30(3):193-202. PubMed ID: 16650806
[TBL] [Abstract][Full Text] [Related]
9. Normalization and quantification of differential expression in gene expression microarrays.
Steinhoff C; Vingron M
Brief Bioinform; 2006 Jun; 7(2):166-77. PubMed ID: 16772260
[TBL] [Abstract][Full Text] [Related]
10. Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays.
Barrera L; Benner C; Tao YC; Winzeler E; Zhou Y
BMC Bioinformatics; 2004 Apr; 5():42. PubMed ID: 15099405
[TBL] [Abstract][Full Text] [Related]
11. Empirical Bayes screening of many p-values with applications to microarray studies.
Datta S; Datta S
Bioinformatics; 2005 May; 21(9):1987-94. PubMed ID: 15691856
[TBL] [Abstract][Full Text] [Related]
12. Sample size calculations based on ranking and selection in microarray experiments.
Matsui S; Zeng S; Yamanaka T; Shaughnessy J
Biometrics; 2008 Mar; 64(1):217-26. PubMed ID: 17680829
[TBL] [Abstract][Full Text] [Related]
13. Pathway analysis using random forests classification and regression.
Pang H; Lin A; Holford M; Enerson BE; Lu B; Lawton MP; Floyd E; Zhao H
Bioinformatics; 2006 Aug; 22(16):2028-36. PubMed ID: 16809386
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. SEGS: search for enriched gene sets in microarray data.
Trajkovski I; Lavrac N; Tolar J
J Biomed Inform; 2008 Aug; 41(4):588-601. PubMed ID: 18234563
[TBL] [Abstract][Full Text] [Related]
16. Mass distributed clustering: a new algorithm for repeated measurements in gene expression data.
Matsumoto S; Aisaki K; Kanno J
Genome Inform; 2005; 16(2):183-94. PubMed ID: 16901101
[TBL] [Abstract][Full Text] [Related]
17. Small, fuzzy and interpretable gene expression based classifiers.
Vinterbo SA; Kim EY; Ohno-Machado L
Bioinformatics; 2005 May; 21(9):1964-70. PubMed ID: 15661797
[TBL] [Abstract][Full Text] [Related]
18. Considerations when using the significance analysis of microarrays (SAM) algorithm.
Larsson O; Wahlestedt C; Timmons JA
BMC Bioinformatics; 2005 May; 6():129. PubMed ID: 15921534
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Two-part permutation tests for DNA methylation and microarray data.
Neuhäuser M; Boes T; Jöckel KH
BMC Bioinformatics; 2005 Feb; 6():35. PubMed ID: 15725357
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]