Microarray Image Analysis: An Algorithmic Approach / Edition 1

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Overview

To harness the high-throughput potential of DNA microarray technology, it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance. Microarray Image Analysis: An Algorithmic Approach presents an automatic system for microarray image processing to make this decoupling a reality. The proposed system integrates and extends traditional analytical-based methods and custom-designed novel algorithms.

The book first explores a new technique that takes advantage of a multiview approach to image analysis and addresses the challenges of applying powerful traditional techniques, such as clustering, to full-scale microarray experiments. It then presents an effective feature identification approach, an innovative technique that renders highly detailed surface models, a new approach to subgrid detection, a novel technique for the background removal process, and a useful technique for removing "noise." The authors also develop an expectation–maximization (EM) algorithm for modeling gene regulatory networks from gene expression time series data. The final chapter describes the overall benefits of these techniques in the biological and computer sciences and reviews future research topics.

This book systematically brings together the fields of image processing, data analysis, and molecular biology to advance the state of the art in this important area. Although the text focuses on improving the processes involved in the analysis of microarray image data, the methods discussed can be applied to a broad range of medical and computer vision analysis areas.

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Editorial Reviews

From the Publisher
Overall, this is a well-written book, and it should be useful for researchers and practitioners who work on microarray image analysis.
—Peihua Qiu, Technometrics, May 2012
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Product Details

Meet the Author

Karl Fraser is a research fellow in the Centre for Intelligent Data Analysis at Brunel University.

Zidong Wang is a professor of dynamical systems and computing in the Department of Information Systems and Computing at Brunel University.

Xiaohu Liu is a professor of computing and head of the Centre for Intelligent Data Analysis at Brunel University.

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Table of Contents

Introduction
Overview
Current state of art
Experimental approach
Key issues
Contribution to knowledge
Structure of the book

Background
Introduction
Molecular biology
Microarray technology
Microarray analysis
Copasetic microarray analysis framework overview
Summary

Data Services
Introduction
Image transformation engine
Evaluation
Summary

Structure Extrapolation I
Introduction
Pyramidic contextual clustering
Evaluation
Summary

Structure Extrapolation II
Introduction
Image layout—master blocks
Image structure—meta-blocks
Summary

Feature Identification I
Introduction
Spatial binding
Evaluation of feature identification
Evaluation of copasetic microarray analysis framework
Summary

Feature Identification II
Background
Proposed approach—subgrid detection
Experimental results
Conclusions

Chained Fourier Background Reconstruction
Introduction
Existing techniques
A new technique
Experiments and results
Conclusions

Graph-Cutting for Improving Microarray Gene Expression
Reconstructions
Introduction
Existing techniques
Proposed technique
Experiments and results
Conclusions

Stochastic Dynamic Modeling of Short Gene Expression Time Series Data
Introduction
Stochastic dynamic model for gene expression data
An EM algorithm for parameter identification
Simulation results
Discussions
Conclusions and future work

Conclusions
Introduction
Achievements
Contributions to microarray biology domain
Contributions to computer science domain
Future research topics

Appendix A: Microarray Variants
Appendix B: Basic Transformations
Appendix C: Clustering
Appendix D: A Glance on Mining Gene Expression Data
Appendix E: Autocorrelation and GHT

References

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