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Author:   Luis Rueda (School of Computer Science, University of Windsor, Ontario, Canada)
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
Volume:   8
Dimensions:   Width: 15.60cm , Height: 3.30cm , Length: 23.40cm
Weight:   0.884kg
ISBN:  

9781466586826

ISBN 10:   1466586826
Pages:   520
Publication Date:   06 March 2014
Audience:   College/higher education ,  General/trade ,  Tertiary & Higher Education ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Introduction to Microarrays. Biological Aspects: Types and Applications of Microarrays. Gridding Methods for DNA Microarray Images. Machine Learning-Based DNA Microarray Image Gridding. Non-Statistical Segmentation Methods for DNA Microarray Images. Statistical Segmentation Methods for DNA Microarray Images. Microarray Image Restoration and Noise Filtering. Compression of DNA Microarray Images. Image Processing of Affymetrix Microarrays. Treatment of Noise and Artifacts in Affymetrix Arrays. Quality Control and Analysis Algorithms for Tissue Microarrays. CNV-Interactome-Transcriptome Integration. Mining Gene-Sample-Time Microarray Data. Systematic and Stochastic Biclustering Algorithms for Microarray Data Analysis. Reconstruction of Regulatory Networks from Microarray Data. Multidimensional Visualization of Microarray Data. Bioconductor Tools for Microarray Data Analysis.

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Author Information

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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