Microarray Data Mining

Microarray Data Mining

Li-Min Fu (Southern California University of Health Sciences, USA)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-010-3.ch190
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Abstract

Based on the concept of simultaneously studying the expression of a large number of genes, a DNA microarray is a chip on which numerous probes are placed for hybridization with a tissue sample. Biological complexity encoded by a deluge of microarray data is being translated into all sorts of computational, statistical or mathematical problems bearing on biological issues ranging from genetic control to signal transduction to metabolism. Microarray data mining is aimed to identify biologically significant genes and find patterns that reveal molecular network dynamics for reconstruction of genetic regulatory networks and pertinent metabolic pathways.
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Main Thrust

The laboratory information management system (LIMS) keeps track of and manages data produced from each step in a microarray experiment, such as hybridization, scanning, and image processing. As microarray experiments generate a vast amount of data, the efficient storage and use of the data require a database management system. While some databases are designed to be data archives only, other databases such as ArrayDB (Ermolaeva et al., 1998) and Argus (Comander, Weber, Gimbrone, & Garcia-Cardena, 2001) allow information storage, query and retrieval as well as data processing, analysis and visualization. These databases also provide a means to link microarray data to other bioinformatics databases (e.g., NCBI Entrez systems, Unigene, KEGG, OMIM). The integration with external information is instrumental to the interpretation of patterns recognized in the gene-expression data. To facilitate the development of microarray databases and analysis tools, there is a need to establish a standard for recording and reporting microarray gene expression data. The MIAME (Minimum Information about Microarray Experiments) standard includes a description of experimental design, array design, samples, hybridization, measurements and normalization controls (Brazma et al., 2001).

Data Mining Objectives

Data mining addresses the question of how to discover a gold mine from historical or experimental data, particularly in a large database. The goal of data mining and knowledge discovery algorithms is to extract implicit, previously unknown and nontrivial patterns, regularities, or knowledge from large data sets that can be used to improve strategic planning and decision-making. The discovered knowledge capturing the relations among the variables of interest can be formulated as a function for making prediction and classification or as a model for understanding the problem in a given domain. In the context of microarray data, the objectives are identifying significant genes and finding gene expression patterns associated with known or unknown categories. Microarray data mining is an important topic in bioinformatics, a field dealing with information processing on biological data, particularly, genomic data.

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