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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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Add 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 prese, Microarray Image Analysis: An Algorithmic Approach to your collection on WonderClub |