Microarray Image and Data Analysis

ISBN 10: 1138374806 / ISBN 13: 9781138374805

Published by CRC Press, 2019

About this Item

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Bibliographic Details

• Title: Microarray Image and Data Analysis
• Publisher: CRC Press
• Publication Date: 2019
• Language: English
• ISBN 10: 1138374806
• ISBN 13: 9781138374805
• Binding: Soft cover
• 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.