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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Microarray technology is a major experimental tool for functional genomic explorations, and should continue to be a major tool throughout the 21st century. The explosion of this technology threatens to overwhelm the scientific community with massive quantities of data, but because microarray data analysis is an emerging field, very few analytical models exist. In this text, readers can learn about up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques. The book 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.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Microarray technology is a major experimental tool for functional genomic explorations, and should continue to be a major tool throughout the 21st century. The explosion of this technology threatens to overwhelm the scientific community with massive quantities of data, but because microarray data analysis is an emerging field, very few analytical models exist. In this text, readers can learn about up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques. The book 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.