Microarray Image Analysis: An Algorithmic Approach Book

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