Microarray Image Analysis (Chapman & Hall/CRC Computer Science & Data Analysis v. 16)
By: Xiaohu Liu (author), Zidong Wang (author), Karl Fraser (author)Hardback
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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.
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.
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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