1st Edition

Microarray Image and Data Analysis Theory and Practice

Edited By Luis Rueda Copyright 2014
    520 Pages 137 B/W Illustrations
    by CRC Press

    520 Pages 137 B/W Illustrations
    by CRC Press

    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.




    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 Hern´andez-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, Rapha¨ele Millat-Carus, Franc¸ois Bertucci, Pascal Finetti, Arnaud Guille, Jos´e Ad´ela¨ıde, 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

    Urˇska Cvek and Marjan Trutschl

    Bioconductor Tools for Microarray Data Analysis

    Simon Cockell, Matthew Bashton, and Colin S. Gillespie



    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.