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Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Genetics (non-medical) > DNA
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A Practical Approach to Microarray Data Analysis (Hardcover, 2003 ed.)
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A Practical Approach to Microarray Data Analysis (Hardcover, 2003 ed.)
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In the past several years, DNA microarray technology has attracted
tremendous interest in both the scientific community and in
industry. With its ability to simultaneously measure the activity
and interactions of thousands of genes, this modern technology
promises unprecedented new insights into mechanisms of living
systems. Currently, the primary applications of microarrays include
gene discovery, disease diagnosis and prognosis, drug discovery
(pharmacogenomics), and toxicological research (toxicogenomics).
Typical scientific tasks addressed by microarray experiments
include the identification of coexpressed genes, discovery of
sample or gene groups with similar expression patterns,
identification of genes whose expression patterns are highly
differentiating with respect to a set of discerned biological
entities (e.g., tumor types), and the study of gene activity
patterns under various stress conditions (e.g., chemical
treatment). More recently, the discovery, modeling, and simulation
of regulatory gene networks, and the mapping of expression data to
metabolic pathways and chromosome locations have been added to the
list of scientific tasks that are being tackled by microarray
technology. Each scientific task corresponds to one or more
so-called data analysis tasks. Different types of scientific
questions require different sets of data analytical techniques.
Broadly speaking, there are two classes of elementary data analysis
tasks, predictive modeling and pattern-detection. Predictive
modeling tasks are concerned with learning a classification or
estimation function, whereas pattern-detection methods screen the
available data for interesting, previously unknown regularities or
relationships.
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