Microarray Image Analysis: An Algorithmic Approach, 1st Edition (Paperback) book cover

Microarray Image Analysis

An Algorithmic Approach, 1st Edition

By Karl Fraser, Zidong Wang, Xiaohui Liu

Chapman and Hall/CRC

335 pages | 134 B/W Illus.

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Description

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.

Reviews

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

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

About the Authors

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.

About the Series

Chapman & Hall/CRC Computer Science & Data Analysis

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Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
SCI008000
SCIENCE / Life Sciences / Biology / General
SCI010000
SCIENCE / Biotechnology