The microarray data analysis process: From raw data to biological significance

cDNA Microarray Data Normalization

Microarray Data Mining for Biological Pathway Analysis

In recent years, microarray gene expression studies have been actively pursued for extracting significant biological knowledge hidden under a large volume of gene expression profiles accumulated by DNA microarray experiments. Particularly great attentions have been paid to a variety of data mining schemes for gene function discovery [Eisen et al., 1998], disease diagnosis [Saiki et al., 2008], ...

Microarray data analysis: Gaining biological insights

DNA microarray is a widely used technique which allows one to identify the genes that are similarly or differentially expressed in different cell types or conditions, to learn how their expression levels change in different developmental stages or disease states, and to identify the cellular processes in which they participate. This technology produces a large amount of complex data, necessitat...

Discovering biological processes from microarray data using independent component analysis

We propose a hypothesis-free methodology for discovering genome-wide expression patterns specific to underlying biological processes from DNA microarray expression data. We apply linear and nonlinear independent component analysis (ICA) as a tool for decomposing microarray data into statistically independent components. Each component represents a gene expression pattern of a putative underlyin...

Parallel Data Mining of microarray biological data

Fundamental research has made huge efforts in order to analyze big amounts of data stored in electronic databases, developing the so-called ”data mining” techniques. At the same time, in recent years, an enormous quantity of biological data has been produced, by means of some techniques recently developed in order to analyze cellular gene expression. The analysis of such data can not be perferm...