CiteSeerX — Search Results

Results 1 - 10 of 8,240

Limma: linear models for microarray data

by Gordon K. Smyth, Matthew Ritchie, Natalie Thorne, James Wettenhall, Wei Shi - Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005

"... This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."

Abstract - Cited by 774 (13 self) - Add to MetaCart

This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents

Analysis of variance for gene expression microarray data

by M. Kathleen Kerr, Mitchell Martin, Gary A. Churchill - Journal of Computational Biology , 2000

"... Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for largescale analysis of gene expression. Microarrays can be used to measure the relative quantities of speci � c mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technolog ..."

Abstract - Cited by 362 (5 self) - Add to MetaCart

of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent “noise ” in microarray data, how

Replicated Microarray Data

by Ingrid Lönnstedt, Terry Speed - Statistica Sinica , 2001

"... cDNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many dierent contexts to compare mRNA levels between two or more samples of cells. Microarray experiments typically give us expression measurements on a large number of genes, say 10,000-20, ..."

Abstract - Cited by 210 (9 self) - Add to MetaCart

microarray data. Data from all the genes in a replicate set of experiments are combined into estimates of parameters of a prior distribution. These parameter estimates are then combined at the gene level with means and standard deviations to form a statistic B which can be used to decide whether dierential

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

by Yee Hwa Yang, Sandrine Dudoit, Percy Luu, Vivian Peng , 2002

"... There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is ..."

Abstract - Cited by 718 (9 self) - Add to MetaCart

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment

Normalization of cDNA microarray data

by Gordon K. Smyth, Terry Speed - Methods , 2003

"... Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This article describes normalization methods based on the fact that dye balance typically varies with ..."

Abstract - Cited by 242 (8 self) - Add to MetaCart

Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This article describes normalization methods based on the fact that dye balance typically varies

of Microarray Data

by Yihui Liu

"... We detect key information of high-dimensional microarray profiles based on wavelet analysis and genetic algorithm. Firstly, wavelet transform is employed to extract approximation coefficients at 2nd level, which remove noise and reduce dimensionality. Genetic algorithm (GA) is performed to select th ..."

Abstract - Add to MetaCart

the optimized features. Experiments are performed on four datasets, and experimental results prove that approximation coefficients are efficient way to characterize the microarray data. Furthermore, in order to detect the key genes in the classification of cancer tissue, we reconstruct the approximation part

Java treeview—extensible visualization of microarray data

by Alok J. Saldanha - Bioinformatics , 2004

"... Summary: Open source software encourages innovation by allowing users to extend the functionality of existing applica-tions. Treeview is a popular application for the visualization of microarray data, but is closed-source and platform-specific, which limits both its current utility and suitability a ..."

Abstract - Cited by 274 (0 self) - Add to MetaCart

Summary: Open source software encourages innovation by allowing users to extend the functionality of existing applica-tions. Treeview is a popular application for the visualization of microarray data, but is closed-source and platform-specific, which limits both its current utility and suitability