Microarray Image and Data Analysis : Theory and Practice

Rueda, Luis (EDT)

ISBN 10: 1466586826 / ISBN 13: 9781466586826

Published by CRC Press, 2014

About this Item

Seller Inventory # 20866429-n

Bibliographic Details

Title: Microarray Image and Data Analysis : Theory ...

Publisher: CRC Press

Publication Date: 2014

Binding: Hardcover

Condition: New

About this title

Synopsis:

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:

• Describes the key stages of image processing, gridding, segmentation, compression, quantification, and normalization
• Features cutting-edge approaches to clustering, biclustering, and the reconstruction of regulatory networks
• Covers different types of microarrays such as DNA, protein, tissue, and low- and high-density oligonucleotide arrays
• Examines the current state of various microarray technologies, including their availability and affordability
• Explains how data generated by microarray experiments are analyzed to obtain meaningful biological conclusions

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.

About the Author:

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.