From microarray data to results

cDNA Microarray Data Normalization

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...

Extracting meaning from microarray data.

Gene expression is complex: many mRNAs change in abundance in response to a new condition. But while some of these expression changes may be direct, many may be downstream, indirect effects. One of the major problems of microarray data analysis is distinguishing between these changes. Some of the most common methods of analysis are discussed, in the context of their ability to distinguish betwe...

Introduction to Microarray Data Mining

-DNA Microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting information on this genomic scale, and a first step towards the rapid and comprehensive interpretation of this data is gene clustering with respect to the expression patterns. Classifying genes into clusters can lead to interest...

A Deterministic Model to Infer Gene Networks from Microarray Data

Microarray experiments help researches to construct the structure of gene regulatory networks, i.e., networks representing relationships among di erent genes. Filter and knowledge extraction processes are necessary in order to handle the huge amount of data produced by microarray technologies. We propose regression trees techniques as a method to identify gene networks. Regression trees are a v...