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Microarray Image and Data Analysis

Theory and Practice

Luis Rueda (School of Computer Science, University of Windsor, Ontario, Canada)

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CRC Press Inc
06 March 2014
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.
Edited by:   Luis Rueda (School of Computer Science University of Windsor Ontario Canada)
Imprint:   CRC Press Inc
Country of Publication:   United States
Volume:   8
Dimensions:   Height: 235mm,  Width: 156mm,  Spine: 33mm
Weight:   885g
ISBN:   9781466586826
ISBN 10:   1466586826
Series:   Digital Imaging and Computer Vision
Pages:   520
Publication Date:   06 March 2014
Audience:   College/higher education ,  College/higher education ,  Primary ,  Primary
Format:   Hardback
Publisher's Status:   Active
Preface Editor Contributors Introduction to Microarrays Luis Rueda and Adnan Ali Biological Aspects: Types and Applications of Microarrays Adnan Ali Gridding Methods for DNA Microarray Images Iman Rezaeian and Luis Rueda Machine Learning-Based DNA Microarray Image Gridding Dimitris Bariamis, Michalis Savelonas, and Dimitris Maroulis Non-Statistical Segmentation Methods for DNA Microarray Images Shahram Shirani Statistical Segmentation Methods for DNA Microarray Images Meng-Yuan Tsai, Tai-Been Chen, and Henry Horng-Shing Lu Microarray Image Restoration and Noise Filtering Rastislav Lukac Compression of DNA Microarray Images Miguel Hernandez-Cabronero, Michael W. Marcellin, and Joan Serra-Sagrist`a Image Processing of Affymetrix Microarrays Jose Manuel Arteaga-Salas Treatment of Noise and Artifacts in Affymetrix Arrays Caroline C. Friedel Quality Control and Analysis Algorithms for Tissue Microarrays as Biomarker Validation Tools Todd H. Stokes, Sonal Kothari, Chih-wen Cheng, and May D. Wang CNV-Interactome-Transcriptome Integration to Detect Driver Genes in Cancerology Maxime Garcia, Raphaele Millat-Carus, Francois Bertucci, Pascal Finetti, Arnaud Guille, Jose Adelaide, Ismahane Bekhouche, Renaud Sabatier, Max Chaffanet, Daniel Birnbaum, and Ghislain Bidaut Mining Gene-Sample-Time Microarray Data Yifeng Li and Alioune Ngom Systematic and Stochastic Biclustering Algorithms for Microarray Data Analysis Wassim Ayadi, Mourad Elloumi, and Jin-Kao Hao Reconstruction of Regulatory Networks from Microarray Data Yiqian Zhou, Rehman Qureshi, Francis Bell, and Ahmet Sacan Multidimensional Visualization of Microarray Data Ur ska Cvek and Marjan Trutschl Bioconductor Tools for Microarray Data Analysis Simon Cockell, Matthew Bashton, and Colin S. Gillespie Index

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 Concepcion, 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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