Microarray technology is a major experimental tool for
functional genomic explorations, and will continue to be a major
tool throughout this decade and beyond. The recent explosion of
this technology threatens to overwhelm the scientific community
with massive quantities of data. Because microarray data analysis
is an emerging field, very few analytical models currently exist.
Methods of Microarray Data Analysis II is the second book in this
pioneering series dedicated to this exciting new field. In a single
reference, readers can learn about the most up-to-date methods,
ranging from data normalization, feature selection, and
discriminative analysis to machine learning techniques.
Currently, there are no standard procedures for the design and
analysis of microarray experiments. Methods of Microarray Data
Analysis II focuses on a single data set, using a different method
of analysis in each chapter. Real examples expose the strengths and
weaknesses of each method for a given situation, aimed at helping
readers choose appropriate protocols and utilize them for their own
data set. In addition, web links are provided to the programs and
tools discussed in several chapters. This book is an excellent
reference not only for academic and industrial researchers, but
also for core bioinformatics/genomics courses in undergraduate and
graduate programs.
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