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.
| Preface |
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ix | |
| Editor |
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xiii | |
| Contributors |
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xv | |
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Chapter 1 Introduction to Microarrays |
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1 | (40) |
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Chapter 2 Biological Aspects: Types and Applications of Microarrays |
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41 | (36) |
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Chapter 3 Gridding Methods for DNA Microarray Images |
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77 | (32) |
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Chapter 4 Machine Learning-Based DNA Microarray Image Gridding |
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109 | (20) |
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Chapter 5 Non-Statistical Segmentation Methods for DNA Microarray Images |
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129 | (20) |
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Chapter 6 Statistical Segmentation Methods for DNA Microarray Images |
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149 | (22) |
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Chapter 7 Microarray Image Restoration and Noise Filtering |
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171 | (24) |
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Chapter 8 Compression of DNA Microarray Images |
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195 | (60) |
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Miguel Hernandez-Cabronero |
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Chapter 10 Treatment of Noise and Artifacts in Affymetrix Arrays |
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255 | (28) |
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Chapter 11 Quality Control and Analysis Algorithms for Tissue Microarrays as Biomarker Validation Tools |
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283 | (30) |
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Chapter 12 CNV-Interactome-Transcriptome Integration to Detect Driver Genes in Cancerology |
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313 | (26) |
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Chapter 13 Mining Gene-Sample-Time Microarray Data |
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339 | (30) |
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Chapter 14 Systematic and Stochastic Biclustering Algorithms for Microarray Data Analysis |
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369 | (32) |
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Chapter 15 Reconstruction of Regulatory Networks from Microarray Data |
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401 | (30) |
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Chapter 16 Multidimensional Visualization of Microarray Data |
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431 | (28) |
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Chapter 17 Bioconductor Tools for Microarray Data Analysis |
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459 | (22) |
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| Index |
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481 | |
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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