Basic Principles and Applications of Microarrays in Medicine

Basic Principles and Applications of Microarrays in Medicine

Andriani Daskalaki (Max Planck Institute of Molecular Genetics, Germany) and Athina A. Lazakidou (University of Piraeus, Greece)
DOI: 10.4018/978-1-59140-982-3.ch046
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The simultaneous expression of a large number of genes is a critical component of normal growth and development, and the maintenance of health. Microarray technology is used to understand fundamental aspects of growth and development, as well as to explore the underlying genetic causes of many human diseases. Systematic analysis of microarray data will yield insight into molecular biological processes and the functions of thousands of gene products in parallel. This approach allows for better understanding in cellular signaling, disease classification, diagnosis, and prognosis. Microarrays allow scientists to analyze the expression of many genes in a single experiment quickly and efficiently. One important goal of computational analysis of microarrays is to extract clues from microarray data and translate the information into biological understanding diseases in medicine and dentistry. There are different platforms or types of DNA microarrays that are commercially available: Glass DNA microarrays and high-density oligonucleotide microarrays. DNA microarray experiments generate large quantities of genome-wide data. To extract useful information from expression profiles, computational tools that compute, statistically validate and display data can be used. An important step in the computation of microarray data is normalization. The purpose of the normalization prozess is to identify and remove the effects of systematic variation in the measured fluorescence intensities other than differential expressions. There are different methods for the normalization of data: total intensity normalization, regression normalization, normalization using ratio statistics, and variance stabilization (VSN). A major goal of microarray data analysis is to identify differentially expressed genes. Selecting marker genes is an important issue for disease classification based on gene expression data.

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