Wide coverage of traditional unsupervised and supervised methods
and newer contemporary approaches that help researchers handle the
rapid growth of classification methods in DNA microarray
Proliferating classification methods in DNA microarray studies
have resulted in a body of information scattered throughout
literature, conference proceedings, and elsewhere. This book unites
many of these classification methods in a single volume. In
addition to traditional statistical methods, it covers newer
machine-learning approaches such as fuzzy methods, artificial
neural networks, evolutionary-based genetic algorithms, support
vector machines, swarm intelligence involving particle swarm
optimization, and more.
"Classification Analysis of DNA Microarrays "provides highly
detailed pseudo-code and rich, graphical programming features, plus
ready-to-run source code. Along with primary methods that include
traditional and contemporary classification, it offers
supplementary tools and data preparation routines for
standardization and fuzzification; dimensional reduction via crisp
and fuzzy c-means, PCA, and non-linear manifold learning; and
computational linguistics via text analytics and n-gram analysis,
recursive feature extraction during ANN, kernel-based methods,
ensemble classifier fusion.
This powerful new resource: Provides information on the use of
classification analysis for DNA microarrays used for large-scale
high-throughput transcriptional studiesServes as a historical
repository of general use supervised classification methods as well
as newer contemporary methodsBrings the reader quickly up to speed
on the various classification methods by implementing the
programming pseudo-code and source code provided in the
bookDescribes implementation methods that help shorten discovery
"Classification Analysis of DNA Microarrays" is useful for
professionals and graduate students in computer science,
bioinformatics, biostatistics, systems biology, and many related
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