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

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

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

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 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

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

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

Vol. 22 no. 14 2006, pages e184–e190 doi:10.1093/bioinformatics/btl230 Predicting the prognosis of breast cancer by integrating clinical

Abstract not found

© 2004 Berger et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License