Interpretation of microarray data in cancer

cDNA Microarray Data Normalization

Analysis and Interpretation of Microarray Data

Diagnosis of Breast Cancer Subtypes using the Selection of Effective Genes from Microarray Data

Introduction: Early diagnosis of breast cancer and the identification of effective genes are important issues in the treatment and survival of the patients. Gene expression data obtained using DNA microarray in combination with machine learning algorithms can provide new and intelligent methods for diagnosis of breast cancer. Methods: Data on the expression of 9216 genes from 84 patients across...

A Survey of Computational Methods used in Microarray Data Interpretation

In a companion chapter in this volume, Wilson et al. (this volume, chapter by Wilson et al.) provide a detailed account of the experimental design and statistical analysis of microarray data. Their chapter is of interest to researchers planning microarray experiments capable of yielding data that can be statistically analyzed to insure reliable levels of confidence. In contrast, the present cha...

Interpretation of ANOVA models for microarray data using PCA

MOTIVATION ANOVA is a technique, which is frequently used in the analysis of microarray data, e.g. to assess the significance of treatment effects, and to select interesting genes based on P-values. However, it does not give information about what exactly is causing the effect. Our purpose is to improve the interpretation of the results from ANOVA on large microarray datasets, by applying PCA o...