Microarray Image Analysis: An Algorithmic Approach

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