Microarray Image Analysis An Algorithmic Approach

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.

IntroductionOverview Current state of art Experimental approach Key issues Contribution to knowledge Structure of the book BackgroundIntroductionMolecular biologyMicroarray technologyMicroarray analysis Copasetic microarray analysis framework overview Summary Data ServicesIntroductionImage transformation engineEvaluationSummary Structure Extrapolation IIntroductionPyramidic contextual clusteringEvaluationSummary Structure Extrapolation IIIntroductionImage layout—master blocksImage structure—meta-blocksSummary Feature Identification IIntroductionSpatial bindingEvaluation of feature identificationEvaluation of copasetic microarray analysis frameworkSummary Feature Identification IIBackgroundProposed approach—subgrid detectionExperimental resultsConclusions Chained Fourier Background ReconstructionIntroductionExisting techniquesA new techniqueExperiments and resultsConclusions Graph-Cutting for Improving Microarray Gene ExpressionReconstructionsIntroductionExisting techniquesProposed techniqueExperiments and resultsConclusions Stochastic Dynamic Modeling of Short Gene Expression Time Series DataIntroductionStochastic dynamic model for gene expression dataAn EM algorithm for parameter identificationSimulation resultsDiscussionsConclusions and future work ConclusionsIntroductionAchievementsContributions to microarray biology domainContributions to computer science domainFuture research topics Appendix A: Microarray VariantsAppendix B: Basic TransformationsAppendix C: ClusteringAppendix D: A Glance on Mining Gene Expression DataAppendix E: Autocorrelation and GHT References

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.