Author Description

<p><b>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 studies</b></p> <p>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.</p> <p><i>Classification Analysis of DNA Microarrays</i> 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.</p> <p>This powerful new resource:</p> <ul> <li>Provides information on the use of classification analysis for DNA microarrays used for large-scale high-throughput transcriptional studies</li> <li>Serves as a historical repository of general use supervised classification methods as well as newer contemporary methods</li> <li>Brings the reader quickly up to speed on the various classification methods by implementing the programming pseudo-code and source code provided in the book</li> <li>Describes implementation methods that help shorten discovery times</li> </ul> <p><i>Classification Analysis of DNA Microarrays</i> is useful for professionals and gradu