Microarray Image and Data Analysis : Theory and Practice
Luis Rueda
Sold by AHA-BUCH GmbH, Einbeck, Germany
AbeBooks Seller since August 14, 2006
New - Hardcover
Condition: New
Quantity: 2 available
Add to basketSold by AHA-BUCH GmbH, Einbeck, Germany
AbeBooks Seller since August 14, 2006
Condition: New
Quantity: 2 available
Add to basketNeuware - This book is a compilation of the latest microarray image and data analysis methods from the multidisciplinary international research community. Delivering a detailed discussion of the biological aspects and applications of different types of microarrays, it examines the current state of microarray technology and describes the key stages of image processing, gridding, segmentation, compression, quantification, and normalization. Featuring cutting-edge approaches to clustering and the reconstruction of regulatory networks, the book explains how data generated by microarray experiments are analyzed to obtain meaningful biological conclusions.
Seller Inventory # 9781466586826
Microarray Image and Data Analysis: Theory and Practice is a compilation of the latest and greatest microarray image and data analysis methods from the multidisciplinary international research community. Delivering a detailed discussion of the biological aspects and applications of microarrays, the book:
An essential reference for academia and industry, Microarray Image and Data Analysis: Theory and Practice provides readers with valuable tools and techniques that extend to a wide range of biological studies and microarray platforms.
Luis Rueda is professor for the School of Computer Science, University of Windsor, Ontario, Canada. Before joining the University of Windsor, he earned a Ph.D from Carleton University, Ottawa, Ontario, Canada and spent two years at the University of Concepción, Chile. A member of IEEE, the Association for Computing Machinery, and the International Society for Computational Biology, he holds three patents on data encryption, secrecy, and stealth; has published over 100 journal and conference papers; and has participated in numerous editorial and technical committees. His research is primarily focused on machine learning and pattern recognition in transcriptomics, interactomics, and genomics.
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