These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
261 related articles for article (PubMed ID: 15180936)
21. Combining multiple microarrays in the presence of controlling variables. Park T; Yi SG; Shin YK; Lee S Bioinformatics; 2006 Jul; 22(14):1682-9. PubMed ID: 16705015 [TBL] [Abstract][Full Text] [Related]
22. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Conesa A; Nueda MJ; Ferrer A; Talón M Bioinformatics; 2006 May; 22(9):1096-102. PubMed ID: 16481333 [TBL] [Abstract][Full Text] [Related]
23. Bayesian variable selection for the analysis of microarray data with censored outcomes. Sha N; Tadesse MG; Vannucci M Bioinformatics; 2006 Sep; 22(18):2262-8. PubMed ID: 16845144 [TBL] [Abstract][Full Text] [Related]
24. Differential gene expression detection and sample classification using penalized linear regression models. Wu B Bioinformatics; 2006 Feb; 22(4):472-6. PubMed ID: 16352654 [TBL] [Abstract][Full Text] [Related]
25. Detecting differential gene expression with a semiparametric hierarchical mixture method. Newton MA; Noueiry A; Sarkar D; Ahlquist P Biostatistics; 2004 Apr; 5(2):155-76. PubMed ID: 15054023 [TBL] [Abstract][Full Text] [Related]
26. A practical false discovery rate approach to identifying patterns of differential expression in microarray data. Grant GR; Liu J; Stoeckert CJ Bioinformatics; 2005 Jun; 21(11):2684-90. PubMed ID: 15797908 [TBL] [Abstract][Full Text] [Related]
27. 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]
28. 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]
29. Algorithm to find gene expression profiles of deregulation and identify families of disease-altered genes. Prieto C; Rivas MJ; Sánchez JM; López-Fidalgo J; De Las Rivas J Bioinformatics; 2006 May; 22(9):1103-10. PubMed ID: 16500942 [TBL] [Abstract][Full Text] [Related]
30. Incorporating gene networks into statistical tests for genomic data via a spatially correlated mixture model. Wei P; Pan W Bioinformatics; 2008 Feb; 24(3):404-11. PubMed ID: 18083717 [TBL] [Abstract][Full Text] [Related]
31. A hierarchical semiparametric model for incorporating intergene information for analysis of genomic data. Qu L; Nettleton D; Dekkers JC Biometrics; 2012 Dec; 68(4):1168-77. PubMed ID: 22994883 [TBL] [Abstract][Full Text] [Related]
32. Class discovery and classification of tumor samples using mixture modeling of gene expression data--a unified approach. Alexandridis R; Lin S; Irwin M Bioinformatics; 2004 Nov; 20(16):2545-52. PubMed ID: 15117753 [TBL] [Abstract][Full Text] [Related]
33. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Barry WT; Nobel AB; Wright FA Bioinformatics; 2005 May; 21(9):1943-9. PubMed ID: 15647293 [TBL] [Abstract][Full Text] [Related]
34. Finding disease specific alterations in the co-expression of genes. Kostka D; Spang R Bioinformatics; 2004 Aug; 20 Suppl 1():i194-9. PubMed ID: 15262799 [TBL] [Abstract][Full Text] [Related]
35. A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. McLachlan GJ; Bean RW; Jones LB Bioinformatics; 2006 Jul; 22(13):1608-15. PubMed ID: 16632494 [TBL] [Abstract][Full Text] [Related]
36. 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]
37. On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression. Pan W Bioinformatics; 2003 Jul; 19(11):1333-40. PubMed ID: 12874044 [TBL] [Abstract][Full Text] [Related]
38. A flexible two-stage procedure for identifying gene sets that are differentially expressed. Heller R; Manduchi E; Grant GR; Ewens WJ Bioinformatics; 2009 Apr; 25(8):1019-25. PubMed ID: 19213738 [TBL] [Abstract][Full Text] [Related]
39. Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data. Li H; Luan Y Bioinformatics; 2005 May; 21(10):2403-9. PubMed ID: 15713732 [TBL] [Abstract][Full Text] [Related]
40. A semiparametric approach for marker gene selection based on gene expression data. Guan Z; Zhao H Bioinformatics; 2005 Feb; 21(4):529-36. PubMed ID: 15374863 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]